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See what this model actually costs to serve.

Fit is always evaluated when model sizing is available. GPU rent, paid idle capacity, API crossover, and measured optimization appear only when their required evidence exists. When the API wins, the page says so.

The modelled monthly GPU rent for Phi-3.5-mini-instruct using 2 replicas of 1 x NVIDIA A100 SXM 40 GB is $1,884.69 at 50,000 requests/day.

Fit is always evaluated when model sizing is available. GPU rent, paid idle capacity, API crossover, and measured optimization appear only when their required evidence exists. When the API wins, the page says so.

Model

microsoft/Phi-3.5-mini-instruct

GPU

NVIDIA A100 SXM 40 GB

Requests/day

50,000

GPU count

1

Quantization

FP16

Tokens/request

500 in, 500 out

Charts appear only when evidence existsUtilization defaults to 30%

Configure the workload

Change the inputs, then calculate a shareable result.

Hugging Face model

Utilization assumption

Defaulted to 30% so the page does not manufacture a self-hosting win.

Derived utilization

71%

Idle share paid

28.5%

Utilization assumption30%

A steady production service with real peaks and troughs.

At this level about 70% of every rented GPU hour sits idle, and it is billed at the same rate as a busy one.

Priced configuration

The serving cost is calculated.

No comparable dated API rate is available for this model. The self-host bill remains reproducible without inventing an API baseline.

Model

microsoft/Phi-3.5-mini-instruct

Hardware

1 x NVIDIA A100 SXM 40 GB, FP16

Memory fit

Weights and KV cache stay separate so the fit is reproducible.

7.6 GB
Model weights
3.2 GB
KV cache
12.5 GB
Total required
36 GB
Available
8,192 tokens
Served context used
1.6 GB
Runtime overhead
Exact transformer shape
KV cache method

Reproduce with vLLM

The exact command attached to this result.

vllm serve 'microsoft/Phi-3.5-mini-instruct' --dtype float16 --tensor-parallel-size 1 --max-model-len 8192 --gpu-memory-utilization 0.9

Priced operating point

Utilization and idle share are shown before the token price.

Paid idle capacity

28.5%

417 of 1,461 billed GPU-hours sit idle. The provider still charges every hour.

Busy GPU-hours

1,044

Useful work and paid idle time sum to the billed total above.

71%
Derived utilization
28.5%
Idle share paid
203 output tok/s
Decode ceiling at concurrency 1
Physics ceiling

CEILING at 100% unit utilisation, not expected throughput. Decode is bounded by memory bandwidth and prefill by dense compute. Ideal tensor-parallel scaling is assumed for both. Real decode and prefill throughput are lower.

Decode and prefill ceilings derived from GPU memory bandwidth, dense compute, and model parameters, captured 2026-08-08

$1,884.69
Monthly GPU rent

$1.29/GPU-hour, Verda, on demand
Captured 2026-08-08 (1 day ago)

At configured-shape saturation, 500 input / 500 output tokens

$1.77 / 1M output tokens

At actual utilization

$2.48 / 1M output tokens

See where the money moves

Each available view stays on its own evidence-backed scale. Missing rows remain visibly absent.

Cost vs volume

Each marker is a full engine recomputation. Self-host prices are never connected or interpolated.

Self-host, discrete pointsCurrent traffic

Scroll horizontally to read every volume point.

500 requests per day, 7,609,375 output tokens per month: self-host $942.35, no comparable API bill. 1,500 requests per day, 22,828,125 output tokens per month: self-host $942.35, no comparable API bill. 5,000 requests per day, 76,093,750 output tokens per month: self-host $942.35, no comparable API bill. 15,000 requests per day, 228,281,250 output tokens per month: self-host $942.35, no comparable API bill. 34,637 requests per day, 527,124,373 output tokens per month: self-host $942.35, no comparable API bill. 35,336 requests per day, 537,773,350 output tokens per month: self-host $1,884.69, no comparable API bill. 50,000 requests per day, 760,937,500 output tokens per month: self-host $1,884.69, no comparable API bill. 150,000 requests per day, 2,282,812,500 output tokens per month: self-host $4,711.73, no comparable API bill. 500,000 requests per day, 7,609,375,000 output tokens per month: self-host $14,135.18, no comparable API bill. 1,500,000 requests per day, 22,828,125,000 output tokens per month: self-host $40,520.84, no comparable API bill. 5,000,000 requests per day, 76,093,750,000 output tokens per month: self-host $134,755.34, no comparable API bill.

$134.8K$0500 req/day50,000 req/day5,000,000 req/day
Current self-host bill
$1,884.69
Current cheapest API
No comparable API bill
Monthly output tokens
760,937,500
Source:
  • Verda pricing, one-GPU A100 SXM4 40 GB instance, as of 2026-08-08
  • Decode and prefill ceilings derived from GPU memory bandwidth, dense compute, and model parameters, as of 2026-08-08
+What this chart means

Replica counts change in whole units. Isolated self-host markers preserve each emitted price without drawing a smooth, straight, or step transition at a volume the engine did not emit. Missing API values produce no marker.

Busy vs idle GPU-hours

1,461 billed GPU-hours, split by the engine into useful work and paid idle capacity.

Scroll horizontally to read every chart label and value.

Busy GPU-hours: 1,044, Engine-reported share doing token work. Idle GPU-hours billed: 417, Engine-reported share paid while idle.

  • Busy GPU-hours1,044
  • Idle GPU-hours billed417
Source:
  • Verda pricing, one-GPU A100 SXM4 40 GB instance, as of 2026-08-08
  • Decode and prefill ceilings derived from GPU memory bandwidth, dense compute, and model parameters, as of 2026-08-08
+What this chart means

Busy and idle hours are emitted by the engine and sum to the billed GPU-hours. The provider charges both, so the idle share remains visible beside the monthly rent.

Provider price spread, NVIDIA A100 SXM 40 GB

USD per GPU-hour. Cheapest captured offers and the rate used in this calculation are marked separately.

Scroll horizontally to read every chart label and value.

Lambda Cloud: $1.99, on demand GPU-hour rate, captured 2026-08-08 (1 day ago). Cheapest + Used: Verda: $1.29, on demand GPU-hour rate, captured 2026-08-08 (1 day ago), used by the engine for this calculation.

  • Lambda Cloud$1.99
  • Cheapest + Used: Verda$1.29
Source:
  • Lambda Cloud, on demand, 1 day ago, captured 2026-08-08
  • Verda, on demand, 1 day ago, captured 2026-08-08
+What this chart means

Every rate is for the selected GPU and keeps its provider, tier, source, capture date, and age. A price without a date is not included.

Not measured on this config yet

No site-wide multiplier is applied. Run the exact configuration to create a receipt before comparing its measured baseline with RunInfra.

Run this configuration

Sources and caveats

Every published number keeps its source and date.

Provenance

  • Hugging Face model API revision sha

    As of 2026-08-08

  • Hugging Face model API safetensors.total

    As of 2026-08-08

  • config.json:max_position_embeddings

    As of 2026-08-08

  • config.json:architectures[0]

    As of 2026-08-08

  • NVIDIA A100 product specification

    As of 2026-08-08

  • Hugging Face config.json transformer shape

    As of 2026-08-08

  • RunInfra Engine feasibility runtime-overhead policy

    As of 2026-08-09

  • Decode and prefill ceilings derived from GPU memory bandwidth, dense compute, and model parameters

    As of 2026-08-08

  • vLLM serve CLI documentation

    As of 2026-08-08

  • Verda pricing, one-GPU A100 SXM4 40 GB instance

    As of 2026-08-08

  • NVIDIA T4 product specification

    As of 2026-08-08

  • NVIDIA L4 product specification

    As of 2026-08-08

  • NVIDIA A10 datasheet

    As of 2026-08-08

  • NVIDIA L40S product specification

    As of 2026-08-08

Caveats

  • +Served context is 8192 tokens. It is a serving-capacity decision and is not inferred from average input or output tokens.
  • +KV cache uses the exact transformer-shape formula with 32 layers, 32 KV heads, and head dimension 96.
  • +Runtime overhead adds 1.63 GB to totalRequiredGb. This approximate 15% policy allowance covers CUDA context, activations, and vLLM overhead. Deployment-time profiling, including CUDA graph capture and allocator behavior, can differ.
  • +CEILING at 100% unit utilisation, not expected throughput. Decode is bounded by memory bandwidth and prefill by dense compute. Ideal tensor-parallel scaling is assumed for both. Real decode and prefill throughput are lower.
  • +Compute prefill ceiling: 40826.15864889484 tokens/sec. Bandwidth decode ceiling: 203.47652788151112 tokens/sec.
  • +CalcInput has no GPU provider selector. The calculation uses the lowest supplied on-demand rate for A100-40GB: Verda at $1.29/GPU-hour.
  • +Replica count covers the configured average daily token work. Peak concurrency is absent from CalcInput, so additional replicas needed for burst traffic are not priced.
  • +The utilization control is a break-even capacity scenario. derivedUtilization is separately computed from the configured daily token work.

Priced configuration

The serving cost is calculated.

No comparable dated API rate is available for this model. The self-host bill remains reproducible without inventing an API baseline.

Model

microsoft/Phi-3.5-mini-instruct

Hardware

1 x NVIDIA A100 SXM 40 GB, FP16

Memory fit

Weights and KV cache stay separate so the fit is reproducible.

7.6 GB
Model weights
3.2 GB
KV cache
12.5 GB
Total required
36 GB
Available
8,192 tokens
Served context used
1.6 GB
Runtime overhead
Exact transformer shape
KV cache method

Reproduce with vLLM

The exact command attached to this result.

vllm serve 'microsoft/Phi-3.5-mini-instruct' --dtype float16 --tensor-parallel-size 1 --max-model-len 8192 --gpu-memory-utilization 0.9

Priced operating point

Utilization and idle share are shown before the token price.

Paid idle capacity

28.5%

417 of 1,461 billed GPU-hours sit idle. The provider still charges every hour.

Busy GPU-hours

1,044

Useful work and paid idle time sum to the billed total above.

71%
Derived utilization
28.5%
Idle share paid
203 output tok/s
Decode ceiling at concurrency 1
Physics ceiling

CEILING at 100% unit utilisation, not expected throughput. Decode is bounded by memory bandwidth and prefill by dense compute. Ideal tensor-parallel scaling is assumed for both. Real decode and prefill throughput are lower.

Decode and prefill ceilings derived from GPU memory bandwidth, dense compute, and model parameters, captured 2026-08-08

$1,884.69
Monthly GPU rent

$1.29/GPU-hour, Verda, on demand
Captured 2026-08-08 (1 day ago)

At configured-shape saturation, 500 input / 500 output tokens

$1.77 / 1M output tokens

At actual utilization

$2.48 / 1M output tokens

See where the money moves

Each available view stays on its own evidence-backed scale. Missing rows remain visibly absent.

Cost vs volume

Each marker is a full engine recomputation. Self-host prices are never connected or interpolated.

Self-host, discrete pointsCurrent traffic

Scroll horizontally to read every volume point.

500 requests per day, 7,609,375 output tokens per month: self-host $942.35, no comparable API bill. 1,500 requests per day, 22,828,125 output tokens per month: self-host $942.35, no comparable API bill. 5,000 requests per day, 76,093,750 output tokens per month: self-host $942.35, no comparable API bill. 15,000 requests per day, 228,281,250 output tokens per month: self-host $942.35, no comparable API bill. 34,637 requests per day, 527,124,373 output tokens per month: self-host $942.35, no comparable API bill. 35,336 requests per day, 537,773,350 output tokens per month: self-host $1,884.69, no comparable API bill. 50,000 requests per day, 760,937,500 output tokens per month: self-host $1,884.69, no comparable API bill. 150,000 requests per day, 2,282,812,500 output tokens per month: self-host $4,711.73, no comparable API bill. 500,000 requests per day, 7,609,375,000 output tokens per month: self-host $14,135.18, no comparable API bill. 1,500,000 requests per day, 22,828,125,000 output tokens per month: self-host $40,520.84, no comparable API bill. 5,000,000 requests per day, 76,093,750,000 output tokens per month: self-host $134,755.34, no comparable API bill.

$134.8K$0500 req/day50,000 req/day5,000,000 req/day
Current self-host bill
$1,884.69
Current cheapest API
No comparable API bill
Monthly output tokens
760,937,500
Source:
  • Verda pricing, one-GPU A100 SXM4 40 GB instance, as of 2026-08-08
  • Decode and prefill ceilings derived from GPU memory bandwidth, dense compute, and model parameters, as of 2026-08-08
+What this chart means

Replica counts change in whole units. Isolated self-host markers preserve each emitted price without drawing a smooth, straight, or step transition at a volume the engine did not emit. Missing API values produce no marker.

Busy vs idle GPU-hours

1,461 billed GPU-hours, split by the engine into useful work and paid idle capacity.

Scroll horizontally to read every chart label and value.

Busy GPU-hours: 1,044, Engine-reported share doing token work. Idle GPU-hours billed: 417, Engine-reported share paid while idle.

  • Busy GPU-hours1,044
  • Idle GPU-hours billed417
Source:
  • Verda pricing, one-GPU A100 SXM4 40 GB instance, as of 2026-08-08
  • Decode and prefill ceilings derived from GPU memory bandwidth, dense compute, and model parameters, as of 2026-08-08
+What this chart means

Busy and idle hours are emitted by the engine and sum to the billed GPU-hours. The provider charges both, so the idle share remains visible beside the monthly rent.

Provider price spread, NVIDIA A100 SXM 40 GB

USD per GPU-hour. Cheapest captured offers and the rate used in this calculation are marked separately.

Scroll horizontally to read every chart label and value.

Lambda Cloud: $1.99, on demand GPU-hour rate, captured 2026-08-08 (1 day ago). Cheapest + Used: Verda: $1.29, on demand GPU-hour rate, captured 2026-08-08 (1 day ago), used by the engine for this calculation.

  • Lambda Cloud$1.99
  • Cheapest + Used: Verda$1.29
Source:
  • Lambda Cloud, on demand, 1 day ago, captured 2026-08-08
  • Verda, on demand, 1 day ago, captured 2026-08-08
+What this chart means

Every rate is for the selected GPU and keeps its provider, tier, source, capture date, and age. A price without a date is not included.

Not measured on this config yet

No site-wide multiplier is applied. Run the exact configuration to create a receipt before comparing its measured baseline with RunInfra.

Run this configuration

Sources and caveats

Every published number keeps its source and date.

Provenance

  • Hugging Face model API revision sha

    As of 2026-08-08

  • Hugging Face model API safetensors.total

    As of 2026-08-08

  • config.json:max_position_embeddings

    As of 2026-08-08

  • config.json:architectures[0]

    As of 2026-08-08

  • NVIDIA A100 product specification

    As of 2026-08-08

  • Hugging Face config.json transformer shape

    As of 2026-08-08

  • RunInfra Engine feasibility runtime-overhead policy

    As of 2026-08-09

  • Decode and prefill ceilings derived from GPU memory bandwidth, dense compute, and model parameters

    As of 2026-08-08

  • vLLM serve CLI documentation

    As of 2026-08-08

  • Verda pricing, one-GPU A100 SXM4 40 GB instance

    As of 2026-08-08

  • NVIDIA T4 product specification

    As of 2026-08-08

  • NVIDIA L4 product specification

    As of 2026-08-08

  • NVIDIA A10 datasheet

    As of 2026-08-08

  • NVIDIA L40S product specification

    As of 2026-08-08

Caveats

  • +Served context is 8192 tokens. It is a serving-capacity decision and is not inferred from average input or output tokens.
  • +KV cache uses the exact transformer-shape formula with 32 layers, 32 KV heads, and head dimension 96.
  • +Runtime overhead adds 1.63 GB to totalRequiredGb. This approximate 15% policy allowance covers CUDA context, activations, and vLLM overhead. Deployment-time profiling, including CUDA graph capture and allocator behavior, can differ.
  • +CEILING at 100% unit utilisation, not expected throughput. Decode is bounded by memory bandwidth and prefill by dense compute. Ideal tensor-parallel scaling is assumed for both. Real decode and prefill throughput are lower.
  • +Compute prefill ceiling: 40826.15864889484 tokens/sec. Bandwidth decode ceiling: 203.47652788151112 tokens/sec.
  • +CalcInput has no GPU provider selector. The calculation uses the lowest supplied on-demand rate for A100-40GB: Verda at $1.29/GPU-hour.
  • +Replica count covers the configured average daily token work. Peak concurrency is absent from CalcInput, so additional replicas needed for burst traffic are not priced.
  • +The utilization control is a break-even capacity scenario. derivedUtilization is separately computed from the configured daily token work.

Priced configuration

The serving cost is calculated.

No comparable dated API rate is available for this model. The self-host bill remains reproducible without inventing an API baseline.

Model

microsoft/Phi-3.5-mini-instruct

Hardware

1 x NVIDIA A100 SXM 40 GB, FP16

Memory fit

Weights and KV cache stay separate so the fit is reproducible.

7.6 GB
Model weights
3.2 GB
KV cache
12.5 GB
Total required
36 GB
Available
8,192 tokens
Served context used
1.6 GB
Runtime overhead
Exact transformer shape
KV cache method

Reproduce with vLLM

The exact command attached to this result.

vllm serve 'microsoft/Phi-3.5-mini-instruct' --dtype float16 --tensor-parallel-size 1 --max-model-len 8192 --gpu-memory-utilization 0.9

Priced operating point

Utilization and idle share are shown before the token price.

Paid idle capacity

28.5%

417 of 1,461 billed GPU-hours sit idle. The provider still charges every hour.

Busy GPU-hours

1,044

Useful work and paid idle time sum to the billed total above.

71%
Derived utilization
28.5%
Idle share paid
203 output tok/s
Decode ceiling at concurrency 1
Physics ceiling

CEILING at 100% unit utilisation, not expected throughput. Decode is bounded by memory bandwidth and prefill by dense compute. Ideal tensor-parallel scaling is assumed for both. Real decode and prefill throughput are lower.

Decode and prefill ceilings derived from GPU memory bandwidth, dense compute, and model parameters, captured 2026-08-08

$1,884.69
Monthly GPU rent

$1.29/GPU-hour, Verda, on demand
Captured 2026-08-08 (1 day ago)

At configured-shape saturation, 500 input / 500 output tokens

$1.77 / 1M output tokens

At actual utilization

$2.48 / 1M output tokens

See where the money moves

Each available view stays on its own evidence-backed scale. Missing rows remain visibly absent.

Cost vs volume

Each marker is a full engine recomputation. Self-host prices are never connected or interpolated.

Self-host, discrete pointsCurrent traffic

Scroll horizontally to read every volume point.

500 requests per day, 7,609,375 output tokens per month: self-host $942.35, no comparable API bill. 1,500 requests per day, 22,828,125 output tokens per month: self-host $942.35, no comparable API bill. 5,000 requests per day, 76,093,750 output tokens per month: self-host $942.35, no comparable API bill. 15,000 requests per day, 228,281,250 output tokens per month: self-host $942.35, no comparable API bill. 34,637 requests per day, 527,124,373 output tokens per month: self-host $942.35, no comparable API bill. 35,336 requests per day, 537,773,350 output tokens per month: self-host $1,884.69, no comparable API bill. 50,000 requests per day, 760,937,500 output tokens per month: self-host $1,884.69, no comparable API bill. 150,000 requests per day, 2,282,812,500 output tokens per month: self-host $4,711.73, no comparable API bill. 500,000 requests per day, 7,609,375,000 output tokens per month: self-host $14,135.18, no comparable API bill. 1,500,000 requests per day, 22,828,125,000 output tokens per month: self-host $40,520.84, no comparable API bill. 5,000,000 requests per day, 76,093,750,000 output tokens per month: self-host $134,755.34, no comparable API bill.

$134.8K$0500 req/day50,000 req/day5,000,000 req/day
Current self-host bill
$1,884.69
Current cheapest API
No comparable API bill
Monthly output tokens
760,937,500
Source:
  • Verda pricing, one-GPU A100 SXM4 40 GB instance, as of 2026-08-08
  • Decode and prefill ceilings derived from GPU memory bandwidth, dense compute, and model parameters, as of 2026-08-08
+What this chart means

Replica counts change in whole units. Isolated self-host markers preserve each emitted price without drawing a smooth, straight, or step transition at a volume the engine did not emit. Missing API values produce no marker.

Busy vs idle GPU-hours

1,461 billed GPU-hours, split by the engine into useful work and paid idle capacity.

Scroll horizontally to read every chart label and value.

Busy GPU-hours: 1,044, Engine-reported share doing token work. Idle GPU-hours billed: 417, Engine-reported share paid while idle.

  • Busy GPU-hours1,044
  • Idle GPU-hours billed417
Source:
  • Verda pricing, one-GPU A100 SXM4 40 GB instance, as of 2026-08-08
  • Decode and prefill ceilings derived from GPU memory bandwidth, dense compute, and model parameters, as of 2026-08-08
+What this chart means

Busy and idle hours are emitted by the engine and sum to the billed GPU-hours. The provider charges both, so the idle share remains visible beside the monthly rent.

Provider price spread, NVIDIA A100 SXM 40 GB

USD per GPU-hour. Cheapest captured offers and the rate used in this calculation are marked separately.

Scroll horizontally to read every chart label and value.

Lambda Cloud: $1.99, on demand GPU-hour rate, captured 2026-08-08 (1 day ago). Cheapest + Used: Verda: $1.29, on demand GPU-hour rate, captured 2026-08-08 (1 day ago), used by the engine for this calculation.

  • Lambda Cloud$1.99
  • Cheapest + Used: Verda$1.29
Source:
  • Lambda Cloud, on demand, 1 day ago, captured 2026-08-08
  • Verda, on demand, 1 day ago, captured 2026-08-08
+What this chart means

Every rate is for the selected GPU and keeps its provider, tier, source, capture date, and age. A price without a date is not included.

Not measured on this config yet

No site-wide multiplier is applied. Run the exact configuration to create a receipt before comparing its measured baseline with RunInfra.

Run this configuration

Sources and caveats

Every published number keeps its source and date.

Provenance

  • Hugging Face model API revision sha

    As of 2026-08-08

  • Hugging Face model API safetensors.total

    As of 2026-08-08

  • config.json:max_position_embeddings

    As of 2026-08-08

  • config.json:architectures[0]

    As of 2026-08-08

  • NVIDIA A100 product specification

    As of 2026-08-08

  • Hugging Face config.json transformer shape

    As of 2026-08-08

  • RunInfra Engine feasibility runtime-overhead policy

    As of 2026-08-09

  • Decode and prefill ceilings derived from GPU memory bandwidth, dense compute, and model parameters

    As of 2026-08-08

  • vLLM serve CLI documentation

    As of 2026-08-08

  • Verda pricing, one-GPU A100 SXM4 40 GB instance

    As of 2026-08-08

  • NVIDIA T4 product specification

    As of 2026-08-08

  • NVIDIA L4 product specification

    As of 2026-08-08

  • NVIDIA A10 datasheet

    As of 2026-08-08

  • NVIDIA L40S product specification

    As of 2026-08-08

Caveats

  • +Served context is 8192 tokens. It is a serving-capacity decision and is not inferred from average input or output tokens.
  • +KV cache uses the exact transformer-shape formula with 32 layers, 32 KV heads, and head dimension 96.
  • +Runtime overhead adds 1.63 GB to totalRequiredGb. This approximate 15% policy allowance covers CUDA context, activations, and vLLM overhead. Deployment-time profiling, including CUDA graph capture and allocator behavior, can differ.
  • +CEILING at 100% unit utilisation, not expected throughput. Decode is bounded by memory bandwidth and prefill by dense compute. Ideal tensor-parallel scaling is assumed for both. Real decode and prefill throughput are lower.
  • +Compute prefill ceiling: 40826.15864889484 tokens/sec. Bandwidth decode ceiling: 203.47652788151112 tokens/sec.
  • +CalcInput has no GPU provider selector. The calculation uses the lowest supplied on-demand rate for A100-40GB: Verda at $1.29/GPU-hour.
  • +Replica count covers the configured average daily token work. Peak concurrency is absent from CalcInput, so additional replicas needed for burst traffic are not priced.
  • +The utilization control is a break-even capacity scenario. derivedUtilization is separately computed from the configured daily token work.

Priced configuration

The serving cost is calculated.

No comparable dated API rate is available for this model. The self-host bill remains reproducible without inventing an API baseline.

Model

microsoft/Phi-3.5-mini-instruct

Hardware

1 x NVIDIA A100 SXM 40 GB, FP16

Memory fit

Weights and KV cache stay separate so the fit is reproducible.

7.6 GB
Model weights
3.2 GB
KV cache
12.5 GB
Total required
36 GB
Available
8,192 tokens
Served context used
1.6 GB
Runtime overhead
Exact transformer shape
KV cache method

Reproduce with vLLM

The exact command attached to this result.

vllm serve 'microsoft/Phi-3.5-mini-instruct' --dtype float16 --tensor-parallel-size 1 --max-model-len 8192 --gpu-memory-utilization 0.9

Priced operating point

Utilization and idle share are shown before the token price.

Paid idle capacity

28.5%

417 of 1,461 billed GPU-hours sit idle. The provider still charges every hour.

Busy GPU-hours

1,044

Useful work and paid idle time sum to the billed total above.

71%
Derived utilization
28.5%
Idle share paid
203 output tok/s
Decode ceiling at concurrency 1
Physics ceiling

CEILING at 100% unit utilisation, not expected throughput. Decode is bounded by memory bandwidth and prefill by dense compute. Ideal tensor-parallel scaling is assumed for both. Real decode and prefill throughput are lower.

Decode and prefill ceilings derived from GPU memory bandwidth, dense compute, and model parameters, captured 2026-08-08

$1,884.69
Monthly GPU rent

$1.29/GPU-hour, Verda, on demand
Captured 2026-08-08 (1 day ago)

At configured-shape saturation, 500 input / 500 output tokens

$1.77 / 1M output tokens

At actual utilization

$2.48 / 1M output tokens

See where the money moves

Each available view stays on its own evidence-backed scale. Missing rows remain visibly absent.

Cost vs volume

Each marker is a full engine recomputation. Self-host prices are never connected or interpolated.

Self-host, discrete pointsCurrent traffic

Scroll horizontally to read every volume point.

500 requests per day, 7,609,375 output tokens per month: self-host $942.35, no comparable API bill. 1,500 requests per day, 22,828,125 output tokens per month: self-host $942.35, no comparable API bill. 5,000 requests per day, 76,093,750 output tokens per month: self-host $942.35, no comparable API bill. 15,000 requests per day, 228,281,250 output tokens per month: self-host $942.35, no comparable API bill. 34,637 requests per day, 527,124,373 output tokens per month: self-host $942.35, no comparable API bill. 35,336 requests per day, 537,773,350 output tokens per month: self-host $1,884.69, no comparable API bill. 50,000 requests per day, 760,937,500 output tokens per month: self-host $1,884.69, no comparable API bill. 150,000 requests per day, 2,282,812,500 output tokens per month: self-host $4,711.73, no comparable API bill. 500,000 requests per day, 7,609,375,000 output tokens per month: self-host $14,135.18, no comparable API bill. 1,500,000 requests per day, 22,828,125,000 output tokens per month: self-host $40,520.84, no comparable API bill. 5,000,000 requests per day, 76,093,750,000 output tokens per month: self-host $134,755.34, no comparable API bill.

$134.8K$0500 req/day50,000 req/day5,000,000 req/day
Current self-host bill
$1,884.69
Current cheapest API
No comparable API bill
Monthly output tokens
760,937,500
Source:
  • Verda pricing, one-GPU A100 SXM4 40 GB instance, as of 2026-08-08
  • Decode and prefill ceilings derived from GPU memory bandwidth, dense compute, and model parameters, as of 2026-08-08
+What this chart means

Replica counts change in whole units. Isolated self-host markers preserve each emitted price without drawing a smooth, straight, or step transition at a volume the engine did not emit. Missing API values produce no marker.

Busy vs idle GPU-hours

1,461 billed GPU-hours, split by the engine into useful work and paid idle capacity.

Scroll horizontally to read every chart label and value.

Busy GPU-hours: 1,044, Engine-reported share doing token work. Idle GPU-hours billed: 417, Engine-reported share paid while idle.

  • Busy GPU-hours1,044
  • Idle GPU-hours billed417
Source:
  • Verda pricing, one-GPU A100 SXM4 40 GB instance, as of 2026-08-08
  • Decode and prefill ceilings derived from GPU memory bandwidth, dense compute, and model parameters, as of 2026-08-08
+What this chart means

Busy and idle hours are emitted by the engine and sum to the billed GPU-hours. The provider charges both, so the idle share remains visible beside the monthly rent.

Provider price spread, NVIDIA A100 SXM 40 GB

USD per GPU-hour. Cheapest captured offers and the rate used in this calculation are marked separately.

Scroll horizontally to read every chart label and value.

Lambda Cloud: $1.99, on demand GPU-hour rate, captured 2026-08-08 (1 day ago). Cheapest + Used: Verda: $1.29, on demand GPU-hour rate, captured 2026-08-08 (1 day ago), used by the engine for this calculation.

  • Lambda Cloud$1.99
  • Cheapest + Used: Verda$1.29
Source:
  • Lambda Cloud, on demand, 1 day ago, captured 2026-08-08
  • Verda, on demand, 1 day ago, captured 2026-08-08
+What this chart means

Every rate is for the selected GPU and keeps its provider, tier, source, capture date, and age. A price without a date is not included.

Not measured on this config yet

No site-wide multiplier is applied. Run the exact configuration to create a receipt before comparing its measured baseline with RunInfra.

Run this configuration

Sources and caveats

Every published number keeps its source and date.

Provenance

  • Hugging Face model API revision sha

    As of 2026-08-08

  • Hugging Face model API safetensors.total

    As of 2026-08-08

  • config.json:max_position_embeddings

    As of 2026-08-08

  • config.json:architectures[0]

    As of 2026-08-08

  • NVIDIA A100 product specification

    As of 2026-08-08

  • Hugging Face config.json transformer shape

    As of 2026-08-08

  • RunInfra Engine feasibility runtime-overhead policy

    As of 2026-08-09

  • Decode and prefill ceilings derived from GPU memory bandwidth, dense compute, and model parameters

    As of 2026-08-08

  • vLLM serve CLI documentation

    As of 2026-08-08

  • Verda pricing, one-GPU A100 SXM4 40 GB instance

    As of 2026-08-08

  • NVIDIA T4 product specification

    As of 2026-08-08

  • NVIDIA L4 product specification

    As of 2026-08-08

  • NVIDIA A10 datasheet

    As of 2026-08-08

  • NVIDIA L40S product specification

    As of 2026-08-08

Caveats

  • +Served context is 8192 tokens. It is a serving-capacity decision and is not inferred from average input or output tokens.
  • +KV cache uses the exact transformer-shape formula with 32 layers, 32 KV heads, and head dimension 96.
  • +Runtime overhead adds 1.63 GB to totalRequiredGb. This approximate 15% policy allowance covers CUDA context, activations, and vLLM overhead. Deployment-time profiling, including CUDA graph capture and allocator behavior, can differ.
  • +CEILING at 100% unit utilisation, not expected throughput. Decode is bounded by memory bandwidth and prefill by dense compute. Ideal tensor-parallel scaling is assumed for both. Real decode and prefill throughput are lower.
  • +Compute prefill ceiling: 40826.15864889484 tokens/sec. Bandwidth decode ceiling: 203.47652788151112 tokens/sec.
  • +CalcInput has no GPU provider selector. The calculation uses the lowest supplied on-demand rate for A100-40GB: Verda at $1.29/GPU-hour.
  • +Replica count covers the configured average daily token work. Peak concurrency is absent from CalcInput, so additional replicas needed for burst traffic are not priced.
  • +The utilization control is a break-even capacity scenario. derivedUtilization is separately computed from the configured daily token work.

Share this calculation

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Calculated by RunInfra

HTML

<a href="https://runinfra.ai/calc/phi-3.5-mini/a100-40gb/50k-req-day?context=8192"><img src="https://runinfra.ai/api/calc/badge?hfId=microsoft%2FPhi-3.5-mini-instruct&amp;gpuId=A100-40GB&amp;gpuCount=1&amp;requestsPerDay=50000&amp;avgInputTokens=500&amp;avgOutputTokens=500&amp;utilization=0.3&amp;quantization=fp16" alt="Calculated by RunInfra"></a>

Markdown

[![Calculated by RunInfra](https://runinfra.ai/api/calc/badge?hfId=microsoft%2FPhi-3.5-mini-instruct&gpuId=A100-40GB&gpuCount=1&requestsPerDay=50000&avgInputTokens=500&avgOutputTokens=500&utilization=0.3&quantization=fp16)](https://runinfra.ai/calc/phi-3.5-mini/a100-40gb/50k-req-day?context=8192)

Use it from an agent or script

GET the current inputs, or inspect the descriptor for accepted parameters and response fields.

Endpoint
GET https://runinfra.ai/api/calchttps://runinfra.ai/api/calc?hfId=microsoft%2FPhi-3.5-mini-instruct&gpuId=A100-40GB&gpuCount=1&requestsPerDay=50000&avgInputTokens=500&avgOutputTokens=500&utilization=0.3&quantization=fp16
Canonical result
https://runinfra.ai/calc/phi-3.5-mini/a100-40gb/50k-req-day?context=8192
Descriptor
https://runinfra.ai/api/calc/schema

28 GPU ids, 7 quantizations

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