VRAM Reference

Qwen 2.5 72B Instruct VRAM Requirements: FP16 / INT4 / KV-Cache (2026)

Qwen 2.5 72B Instruct runs on 72.7B parameters with a 128K-token context window. Weights are deterministic param math; KV-cache uses the registry layer geometry where published, otherwise a modeled GQA estimate — every figure is provenance-labeled below.

FP16 weights140 GB

INT4 weights40 GB

Context128K tokens

Single-GPU verdictFits (INT4, 49 GB)

140 GB FP16 weights

Calculated EstimateHIGH

SourceOpenGPU Radar models-registry.json (param-count weight model)

VerifiedSep 29, 2026

Value

140 GB

Methodology

weights = parametersB × bytes-per-parameter (FP16 = 2 B, INT4 = 0.5 B)

Refreshed daily from provider APIs and market scrapingSep 29, 2026

40 GB INT4 weights

Calculated EstimateHIGH

SourceOpenGPU Radar models-registry.json (param-count weight model)

VerifiedSep 29, 2026

Value

40 GB

Methodology

weights = parametersB × bytes-per-parameter (FP16 = 2 B, INT4 = 0.5 B)

Refreshed daily from provider APIs and market scrapingSep 29, 2026

128K context window

Manufacturer SpecHIGH

SourceModel vendor context-window specification

VerifiedSep 29, 2026

Value

128,000 tokens

Refreshed daily from provider APIs and market scrapingSep 29, 2026

Methodology →

How much VRAM does Qwen 2.5 72B Instruct need?

Weights: 140 GB at FP16, 40 GB at INT4 (parameters × bytes-per-parameter: FP16 = 2 B, INT4 = 0.5 B).

Full stack at 128K: 176.7 GB FP16 / 49.3 GB INT4 — including KV-cache, CUDA overhead, activations, and fragmentation headroom from the canonical VRAM engine.

Single-GPU verdict: INT4 fits 80 GB datacenter cards (A100/H100); FP16 needs 240 GB → multi-GPU.

VRAM by Precision & Context

From the canonical VRAM engine: weights + KV-cache + CUDA overhead + activations + fragmentation headroom. KV-cache uses published layer geometry where available; otherwise a modeled GQA estimate (labeled in the badge).

Precision	Context	Weights	KV-Cache	Total (est.)
FP16	4K	145.4 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 145.4 GB Methodology Formula: parameterCountB × bytesPerParam. Precision: FP16 = 2 bytes/param. (parameterCountB in billions, result in GB). Assumptions & Parameters paramCountB: 72.7 quantization: fp16 bytesPerParam: 2 Refreshed daily from provider APIs and market scrapingSep 26, 2026	0.44 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 0.44 GB Methodology GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1. Assumptions & Parameters numLayers: 28 numKvHeads: 8 headDim: 128 contextLength: 4096 batchSize: 1 quantization: fp16 gqaRatio: 4 Refreshed daily from provider APIs and market scrapingSep 26, 2026	162.2 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 162.18 GB Methodology Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%). Assumptions & Parameters paramCountB: 72.7 quantization: fp16 contextLength: 4096 batchSize: 1 numLayers: 28 numKvHeads: 8 headDim: 128 gqaRatio: 4 architecture: qwen-2.5-72b bytesPerParam: 2 bytesPerKvElement: 2 cudaOverheadGb: 1.2 activationGb: 0.4 fragmentationHeadroomPct: 10 isMla: false Refreshed daily from provider APIs and market scrapingSep 26, 2026
FP16	33K	145.4 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 145.4 GB Methodology Formula: parameterCountB × bytesPerParam. Precision: FP16 = 2 bytes/param. (parameterCountB in billions, result in GB). Assumptions & Parameters paramCountB: 72.7 quantization: fp16 bytesPerParam: 2 Refreshed daily from provider APIs and market scrapingSep 26, 2026	3.50 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 3.5 GB Methodology GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1. Assumptions & Parameters numLayers: 28 numKvHeads: 8 headDim: 128 contextLength: 32768 batchSize: 1 quantization: fp16 gqaRatio: 4 Refreshed daily from provider APIs and market scrapingSep 26, 2026	165.6 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 165.55 GB Methodology Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%). Assumptions & Parameters paramCountB: 72.7 quantization: fp16 contextLength: 32768 batchSize: 1 numLayers: 28 numKvHeads: 8 headDim: 128 gqaRatio: 4 architecture: qwen-2.5-72b bytesPerParam: 2 bytesPerKvElement: 2 cudaOverheadGb: 1.2 activationGb: 0.4 fragmentationHeadroomPct: 10 isMla: false Refreshed daily from provider APIs and market scrapingSep 26, 2026
FP16	128K	145.4 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 145.4 GB Methodology Formula: parameterCountB × bytesPerParam. Precision: FP16 = 2 bytes/param. (parameterCountB in billions, result in GB). Assumptions & Parameters paramCountB: 72.7 quantization: fp16 bytesPerParam: 2 Refreshed daily from provider APIs and market scrapingSep 26, 2026	13.67 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 13.67 GB Methodology GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1. Assumptions & Parameters numLayers: 28 numKvHeads: 8 headDim: 128 contextLength: 128000 batchSize: 1 quantization: fp16 gqaRatio: 4 Refreshed daily from provider APIs and market scrapingSep 26, 2026	176.7 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 176.74 GB Methodology Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%). Assumptions & Parameters paramCountB: 72.7 quantization: fp16 contextLength: 128000 batchSize: 1 numLayers: 28 numKvHeads: 8 headDim: 128 gqaRatio: 4 architecture: qwen-2.5-72b bytesPerParam: 2 bytesPerKvElement: 2 cudaOverheadGb: 1.2 activationGb: 0.4 fragmentationHeadroomPct: 10 isMla: false Refreshed daily from provider APIs and market scrapingSep 26, 2026
INT4	4K	36.4 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 36.35 GB Methodology Formula: parameterCountB × bytesPerParam. Precision: INT4 = 0.5 bytes/param. (parameterCountB in billions, result in GB). Assumptions & Parameters paramCountB: 72.7 quantization: int4 bytesPerParam: 0.5 Refreshed daily from provider APIs and market scrapingSep 26, 2026	0.22 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 0.22 GB Methodology GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1. Assumptions & Parameters numLayers: 28 numKvHeads: 8 headDim: 128 contextLength: 4096 batchSize: 1 quantization: int4 gqaRatio: 4 Refreshed daily from provider APIs and market scrapingSep 26, 2026	42.0 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 41.99 GB Methodology Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%). Assumptions & Parameters paramCountB: 72.7 quantization: int4 contextLength: 4096 batchSize: 1 numLayers: 28 numKvHeads: 8 headDim: 128 gqaRatio: 4 architecture: qwen-2.5-72b bytesPerParam: 0.5 bytesPerKvElement: 1 cudaOverheadGb: 1.2 activationGb: 0.4 fragmentationHeadroomPct: 10 isMla: false Refreshed daily from provider APIs and market scrapingSep 26, 2026
INT4	33K	36.4 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 36.35 GB Methodology Formula: parameterCountB × bytesPerParam. Precision: INT4 = 0.5 bytes/param. (parameterCountB in billions, result in GB). Assumptions & Parameters paramCountB: 72.7 quantization: int4 bytesPerParam: 0.5 Refreshed daily from provider APIs and market scrapingSep 26, 2026	1.75 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 1.75 GB Methodology GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1. Assumptions & Parameters numLayers: 28 numKvHeads: 8 headDim: 128 contextLength: 32768 batchSize: 1 quantization: int4 gqaRatio: 4 Refreshed daily from provider APIs and market scrapingSep 26, 2026	43.7 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 43.67 GB Methodology Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%). Assumptions & Parameters paramCountB: 72.7 quantization: int4 contextLength: 32768 batchSize: 1 numLayers: 28 numKvHeads: 8 headDim: 128 gqaRatio: 4 architecture: qwen-2.5-72b bytesPerParam: 0.5 bytesPerKvElement: 1 cudaOverheadGb: 1.2 activationGb: 0.4 fragmentationHeadroomPct: 10 isMla: false Refreshed daily from provider APIs and market scrapingSep 26, 2026
INT4	128K	36.4 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 36.35 GB Methodology Formula: parameterCountB × bytesPerParam. Precision: INT4 = 0.5 bytes/param. (parameterCountB in billions, result in GB). Assumptions & Parameters paramCountB: 72.7 quantization: int4 bytesPerParam: 0.5 Refreshed daily from provider APIs and market scrapingSep 26, 2026	6.84 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 6.84 GB Methodology GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1. Assumptions & Parameters numLayers: 28 numKvHeads: 8 headDim: 128 contextLength: 128000 batchSize: 1 quantization: int4 gqaRatio: 4 Refreshed daily from provider APIs and market scrapingSep 26, 2026	49.3 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 49.27 GB Methodology Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%). Assumptions & Parameters paramCountB: 72.7 quantization: int4 contextLength: 128000 batchSize: 1 numLayers: 28 numKvHeads: 8 headDim: 128 gqaRatio: 4 architecture: qwen-2.5-72b bytesPerParam: 0.5 bytesPerKvElement: 1 cudaOverheadGb: 1.2 activationGb: 0.4 fragmentationHeadroomPct: 10 isMla: false Refreshed daily from provider APIs and market scrapingSep 26, 2026

Cloud GPUs That Fit Qwen 2.5 72B Instruct

Fit = full-stack total (weights + KV at 128K) ≤ GPU VRAM. Rates are the lowest observed on-demand rows in data/providers.json, refreshed daily.

GPU	VRAM	FP16 fit	INT4 fit	Lowest on-demand
H200 SXM5	141 GB	OOM	✓ fits	$2.79/hr (Vast.ai)
B200 Blackwell	192 GB	✓ fits	✓ fits	$3.99/hr (Vast.ai)
H100 SXM5	80 GB	OOM	✓ fits	$1.89/hr (Vast.ai)
A100 80GB SXM4	80 GB	OOM	✓ fits	$1.59/hr (Lambda Labs)
L40S	48 GB	OOM	OOM	$0.69/hr (Vast.ai)
RTX 4090	24 GB	OOM	OOM	$0.34/hr (Vast.ai)

Recommended GPUs for Qwen 2.5 72B Instruct

Minimum = smallest single GPU that fits the full INT4 stack (49.3 GB). Optimal = registry recommendation, falling back to the lowest observed on-demand rate among fitting cards. Every card links to its canonical GPU page.

Minimum80 GB · $1.89/hr

H100 SXM5

Smallest fitting card — 80 GB vs 49.3 GB INT4 full stack.

View GPU page →

Optimal141 GB · $2.79/hr

H200 SXM5

Registry recommendation (1x H200).

View GPU page →

Workload Scenarios Using Qwen 2.5 72B Instruct

Costed workload guides that price Qwen 2.5 72B Instruct against verified cloud rates and registry VRAM math.

Batch

Batch Inference

Offline batch inference economics: why the cheapest large-VRAM GPU leads when latency is free and preemption risk is priced in.

Next steps

⚡

Open in VRAM calculatorPre-filled with Qwen 2.5 72B Instruct

✓

Full model profileBenchmarks, providers & run commands

GPU

Frequently Asked Questions

How much VRAM does Qwen 2.5 72B Instruct need?▾

Qwen 2.5 72B Instruct needs 140 GB at FP16 (weights) and 40 GB at INT4 from the param-count weight model. Full-stack totals at 128K context are 176.7 GB (FP16) and 49.3 GB (INT4) including KV-cache, CUDA overhead, and fragmentation headroom.

Can Qwen 2.5 72B Instruct run on a single GPU?▾

Yes — the registry recommendation is 1x H200, and the NVIDIA H200 SXM5 (141GB HBM3e) fits the INT4 workload single-GPU. FP16 full-stack needs 176.7 GB.

What context length fits Qwen 2.5 72B Instruct?▾

Registry context window is 128,000 tokens. KV-cache grows linearly with context — the quantization table shows totals at 4K, 33K, 128K context for FP16 and INT4.

Weight model: parameters × bytes-per-parameter. KV-cache: GQA formula or modeled estimate where layer geometry is unpublished.Methodology →