⚡Under $0.50/hr🎁Free LLMs⚖Compare GPUs🎁Free LLM Radar🧪Playground🎯Model Index
VRAM Reference

Qwen 2.5 Coder 32B VRAM Requirements: FP16 / INT4 / KV-Cache (2026)

Qwen 2.5 Coder 32B runs on 32.5B 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 weights64 GB
INT4 weights20 GB
Context128K tokens
Single-GPU verdictFits (INT4, 34 GB)
64 GB FP16 weights
Calculated EstimateHIGH
SourceOpenGPU Radar models-registry.json (param-count weight model)
VerifiedSep 29, 2026
Value
64 GB
Methodology

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

Refreshed daily from provider APIs and market scrapingSep 29, 2026
20 GB INT4 weights
Calculated EstimateHIGH
SourceOpenGPU Radar models-registry.json (param-count weight model)
VerifiedSep 29, 2026
Value
20 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 Coder 32B need?

Weights: 64 GB at FP16, 20 GB at INT4 (parameters × bytes-per-parameter: FP16 = 2 B, INT4 = 0.5 B).

Full stack at 128K: 101.2 GB FP16 / 33.6 GB INT4 — including KV-cache, CUDA overhead, activations, and fragmentation headroom from the canonical VRAM engine.

Single-GPU verdict: INT4 fits 48 GB cards (e.g. L40S); 24 GB cards are too small. FP16 needs 160 GB-class hardware.

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).

PrecisionContextWeightsKV-CacheTotal (est.)
FP164K65.0 GB
Calculated EstimateHIGH
SourceDeterministic VRAM Canonical Engine
VerifiedSep 26, 2026
Value
65 GB
Methodology

Formula: parameterCountB × bytesPerParam. Precision: FP16 = 2 bytes/param. (parameterCountB in billions, result in GB).

Assumptions & Parameters
  • paramCountB: 32.5
  • quantization: fp16
  • bytesPerParam: 2
Refreshed daily from provider APIs and market scrapingSep 26, 2026
0.81 GB
Calculated EstimateHIGH
SourceDeterministic VRAM Canonical Engine
VerifiedSep 26, 2026
Value
0.81 GB
Methodology

GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1.

Assumptions & Parameters
  • numLayers: 52
  • numKvHeads: 8
  • headDim: 128
  • contextLength: 4096
  • batchSize: 1
  • quantization: fp16
  • gqaRatio: 4
Refreshed daily from provider APIs and market scrapingSep 26, 2026
74.2 GB
Calculated EstimateHIGH
SourceDeterministic VRAM Canonical Engine
VerifiedSep 26, 2026
Value
74.15 GB
Methodology

Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%).

Assumptions & Parameters
  • paramCountB: 32.5
  • quantization: fp16
  • contextLength: 4096
  • batchSize: 1
  • numLayers: 52
  • numKvHeads: 8
  • headDim: 128
  • gqaRatio: 4
  • architecture: qwen-2.5-coder-32b
  • bytesPerParam: 2
  • bytesPerKvElement: 2
  • cudaOverheadGb: 1.2
  • activationGb: 0.4
  • fragmentationHeadroomPct: 10
  • isMla: false
Refreshed daily from provider APIs and market scrapingSep 26, 2026
FP1633K65.0 GB
Calculated EstimateHIGH
SourceDeterministic VRAM Canonical Engine
VerifiedSep 26, 2026
Value
65 GB
Methodology

Formula: parameterCountB × bytesPerParam. Precision: FP16 = 2 bytes/param. (parameterCountB in billions, result in GB).

Assumptions & Parameters
  • paramCountB: 32.5
  • quantization: fp16
  • bytesPerParam: 2
Refreshed daily from provider APIs and market scrapingSep 26, 2026
6.50 GB
Calculated EstimateHIGH
SourceDeterministic VRAM Canonical Engine
VerifiedSep 26, 2026
Value
6.5 GB
Methodology

GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1.

Assumptions & Parameters
  • numLayers: 52
  • numKvHeads: 8
  • headDim: 128
  • contextLength: 32768
  • batchSize: 1
  • quantization: fp16
  • gqaRatio: 4
Refreshed daily from provider APIs and market scrapingSep 26, 2026
80.4 GB
Calculated EstimateHIGH
SourceDeterministic VRAM Canonical Engine
VerifiedSep 26, 2026
Value
80.41 GB
Methodology

Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%).

Assumptions & Parameters
  • paramCountB: 32.5
  • quantization: fp16
  • contextLength: 32768
  • batchSize: 1
  • numLayers: 52
  • numKvHeads: 8
  • headDim: 128
  • gqaRatio: 4
  • architecture: qwen-2.5-coder-32b
  • bytesPerParam: 2
  • bytesPerKvElement: 2
  • cudaOverheadGb: 1.2
  • activationGb: 0.4
  • fragmentationHeadroomPct: 10
  • isMla: false
Refreshed daily from provider APIs and market scrapingSep 26, 2026
FP16128K65.0 GB
Calculated EstimateHIGH
SourceDeterministic VRAM Canonical Engine
VerifiedSep 26, 2026
Value
65 GB
Methodology

Formula: parameterCountB × bytesPerParam. Precision: FP16 = 2 bytes/param. (parameterCountB in billions, result in GB).

Assumptions & Parameters
  • paramCountB: 32.5
  • quantization: fp16
  • bytesPerParam: 2
Refreshed daily from provider APIs and market scrapingSep 26, 2026
25.39 GB
Calculated EstimateHIGH
SourceDeterministic VRAM Canonical Engine
VerifiedSep 26, 2026
Value
25.39 GB
Methodology

GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1.

Assumptions & Parameters
  • numLayers: 52
  • numKvHeads: 8
  • headDim: 128
  • contextLength: 128000
  • batchSize: 1
  • quantization: fp16
  • gqaRatio: 4
Refreshed daily from provider APIs and market scrapingSep 26, 2026
101.2 GB
Calculated EstimateHIGH
SourceDeterministic VRAM Canonical Engine
VerifiedSep 26, 2026
Value
101.19 GB
Methodology

Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%).

Assumptions & Parameters
  • paramCountB: 32.5
  • quantization: fp16
  • contextLength: 128000
  • batchSize: 1
  • numLayers: 52
  • numKvHeads: 8
  • headDim: 128
  • gqaRatio: 4
  • architecture: qwen-2.5-coder-32b
  • bytesPerParam: 2
  • bytesPerKvElement: 2
  • cudaOverheadGb: 1.2
  • activationGb: 0.4
  • fragmentationHeadroomPct: 10
  • isMla: false
Refreshed daily from provider APIs and market scrapingSep 26, 2026
INT44K16.3 GB
Calculated EstimateHIGH
SourceDeterministic VRAM Canonical Engine
VerifiedSep 26, 2026
Value
16.25 GB
Methodology

Formula: parameterCountB × bytesPerParam. Precision: INT4 = 0.5 bytes/param. (parameterCountB in billions, result in GB).

Assumptions & Parameters
  • paramCountB: 32.5
  • quantization: int4
  • bytesPerParam: 0.5
Refreshed daily from provider APIs and market scrapingSep 26, 2026
0.41 GB
Calculated EstimateHIGH
SourceDeterministic VRAM Canonical Engine
VerifiedSep 26, 2026
Value
0.41 GB
Methodology

GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1.

Assumptions & Parameters
  • numLayers: 52
  • numKvHeads: 8
  • headDim: 128
  • contextLength: 4096
  • batchSize: 1
  • quantization: int4
  • gqaRatio: 4
Refreshed daily from provider APIs and market scrapingSep 26, 2026
20.1 GB
Calculated EstimateHIGH
SourceDeterministic VRAM Canonical Engine
VerifiedSep 26, 2026
Value
20.08 GB
Methodology

Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%).

Assumptions & Parameters
  • paramCountB: 32.5
  • quantization: int4
  • contextLength: 4096
  • batchSize: 1
  • numLayers: 52
  • numKvHeads: 8
  • headDim: 128
  • gqaRatio: 4
  • architecture: qwen-2.5-coder-32b
  • 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
INT433K16.3 GB
Calculated EstimateHIGH
SourceDeterministic VRAM Canonical Engine
VerifiedSep 26, 2026
Value
16.25 GB
Methodology

Formula: parameterCountB × bytesPerParam. Precision: INT4 = 0.5 bytes/param. (parameterCountB in billions, result in GB).

Assumptions & Parameters
  • paramCountB: 32.5
  • quantization: int4
  • bytesPerParam: 0.5
Refreshed daily from provider APIs and market scrapingSep 26, 2026
3.25 GB
Calculated EstimateHIGH
SourceDeterministic VRAM Canonical Engine
VerifiedSep 26, 2026
Value
3.25 GB
Methodology

GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1.

Assumptions & Parameters
  • numLayers: 52
  • numKvHeads: 8
  • headDim: 128
  • contextLength: 32768
  • batchSize: 1
  • quantization: int4
  • gqaRatio: 4
Refreshed daily from provider APIs and market scrapingSep 26, 2026
23.2 GB
Calculated EstimateHIGH
SourceDeterministic VRAM Canonical Engine
VerifiedSep 26, 2026
Value
23.21 GB
Methodology

Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%).

Assumptions & Parameters
  • paramCountB: 32.5
  • quantization: int4
  • contextLength: 32768
  • batchSize: 1
  • numLayers: 52
  • numKvHeads: 8
  • headDim: 128
  • gqaRatio: 4
  • architecture: qwen-2.5-coder-32b
  • 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
INT4128K16.3 GB
Calculated EstimateHIGH
SourceDeterministic VRAM Canonical Engine
VerifiedSep 26, 2026
Value
16.25 GB
Methodology

Formula: parameterCountB × bytesPerParam. Precision: INT4 = 0.5 bytes/param. (parameterCountB in billions, result in GB).

Assumptions & Parameters
  • paramCountB: 32.5
  • quantization: int4
  • bytesPerParam: 0.5
Refreshed daily from provider APIs and market scrapingSep 26, 2026
12.70 GB
Calculated EstimateHIGH
SourceDeterministic VRAM Canonical Engine
VerifiedSep 26, 2026
Value
12.7 GB
Methodology

GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1.

Assumptions & Parameters
  • numLayers: 52
  • numKvHeads: 8
  • headDim: 128
  • contextLength: 128000
  • batchSize: 1
  • quantization: int4
  • gqaRatio: 4
Refreshed daily from provider APIs and market scrapingSep 26, 2026
33.6 GB
Calculated EstimateHIGH
SourceDeterministic VRAM Canonical Engine
VerifiedSep 26, 2026
Value
33.6 GB
Methodology

Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%).

Assumptions & Parameters
  • paramCountB: 32.5
  • quantization: int4
  • contextLength: 128000
  • batchSize: 1
  • numLayers: 52
  • numKvHeads: 8
  • headDim: 128
  • gqaRatio: 4
  • architecture: qwen-2.5-coder-32b
  • 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 Coder 32B

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

GPUVRAMFP16 fitINT4 fitLowest on-demand
H200 SXM5141 GB✓ fits✓ fits$2.79/hr (Vast.ai)
B200 Blackwell192 GB✓ fits✓ fits$3.99/hr (Vast.ai)
H100 SXM580 GBOOM✓ fits$1.89/hr (Vast.ai)
A100 80GB SXM480 GBOOM✓ fits$1.59/hr (Lambda Labs)
L40S48 GBOOM✓ fits$0.69/hr (Vast.ai)
RTX 409024 GBOOMOOM$0.34/hr (Vast.ai)

Recommended GPUs for Qwen 2.5 Coder 32B

Minimum = smallest single GPU that fits the full INT4 stack (33.6 GB). Optimal = registry recommendation, falling back to the lowest observed on-demand rate among fitting cards. Every card links to its canonical GPU page.

Minimum48 GB · $0.69/hr
L40S

Smallest fitting card — 48 GB vs 33.6 GB INT4 full stack.

View GPU page →

Optimal80 GB · $1.59/hr
A100 80GB SXM4

Registry recommendation (1x A100).

View GPU page →

Workload Scenarios Using Qwen 2.5 Coder 32B

Costed workload guides that price Qwen 2.5 Coder 32B against verified cloud rates and registry VRAM math.

RAG
RAG Pipeline

What a retrieval-augmented generation stack costs per hour: embedding retrieval on shared hardware, generation leg priced on verified GPU rates.

Fine-Tuning
LLM Fine-Tuning

Verified hourly rates for LoRA and QLoRA fine-tuning: which GPUs fit 32B-70B adapters, and where optimizer state beats weights.

Next steps

⚡
Open in VRAM calculatorPre-filled with Qwen 2.5 Coder 32B
✓
Full model profileBenchmarks, providers & run commands
GPU
Recommended GPU1x A100 → NVIDIA A100 80GB SXM4

Frequently Asked Questions

How much VRAM does Qwen 2.5 Coder 32B need?▾
Qwen 2.5 Coder 32B needs 64 GB at FP16 (weights) and 20 GB at INT4 from the param-count weight model. Full-stack totals at 128K context are 101.2 GB (FP16) and 33.6 GB (INT4) including KV-cache, CUDA overhead, and fragmentation headroom.
Can Qwen 2.5 Coder 32B run on a single GPU?▾
Yes — the registry recommendation is 1x A100, and the NVIDIA A100 80GB SXM4 (80GB HBM2e) fits the INT4 workload single-GPU. FP16 full-stack needs 101.2 GB.
What context length fits Qwen 2.5 Coder 32B?▾
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 →