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Run LLM Locally

Qwen2.5 14B Instruct on the H100 PCIe

With 80 GB of VRAM, the H100 PCIe runs Qwen2.5 14B Instruct at Q4_K_M entirely on the GPU with an 8K context (about 11.2 GB needed). At Q8_0 it fits entirely.

Qwen2.5 14B Instruct

Q4_K_M · 8K context

Excellent

The model needs about 11.2 GB and fits in the 77.6 GB available, with 66.4 GB to spare.

Your hardware
H100 PCIe 80 GB · 64 GB RAM
Estimated memory
~11.2 GB estimate
VRAM (usable memory)
77.6 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
109–169 tok/s estimate
Largest context in fast memory
128K tokens
Confidence: low

Based on: memory bandwidth (2,000 GB/s) and model size

Memory breakdown

~11.2 GB (10.7 GB–11.8 GB) needed · estimate

Fits entirely in VRAM · 66.4 GB left

  • Model weights8.4 GB
  • KV cache1.5 GB
  • Runtime overhead0.8 GB
  • Safety margin0.5 GB
Assumptions: context 8K tokens · KV cache f16 · llama.cpp (CUDA) · flash attention on · weights = real file size

EstimateData updated 24 days agoSource: Run LLM Locally engine, Hugging Face config.json + model card

What makes this estimate less reliable

  • no benchmark for this hardware and model yet

Other quantizations on the H100 PCIe

Qwen2.5 14B Instruct on H100 PCIe + 64 GB RAM · 8K context
Qwen2.5 14B Instruct Q8_08K~17.8 GB · 74–107 tok/s estimateExcellent
Qwen2.5 14B Instruct Q6_K8K~14.3 GB · 89–133 tok/s estimateExcellent
Qwen2.5 14B Instruct Q5_K_M8K~12.7 GB · 98–150 tok/s estimateExcellent
Qwen2.5 14B Instruct Q4_K_M8K~11.2 GB · 109–169 tok/s estimateExcellent
Qwen2.5 14B Instruct IQ4_XS8K~10.4 GB · 116–183 tok/s estimateExcellent
Qwen2.5 14B Instruct Q3_K_M8K~9.6 GB · 123–197 tok/s estimateExcellent

Alternatives

Hardware that runs Qwen2.5 14B Instruct Q4_K_M entirely in memory

Prices and links follow the market you choose.

GeForce RTX 4060 Ti 16 GB

NVIDIA

ExcellentRuns Qwen2.5 14B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

Radeon RX 7600 XT

AMD

ExcellentRuns Qwen2.5 14B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

MacBook Pro 14 M3 Pro

Apple Silicon

CompatibleRuns Qwen2.5 14B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

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AMD Ryzen AI Max+ 395

Unified memory PC

ExcellentRuns Qwen2.5 14B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

No current offer imported for this market: the link opens a marketplace search.

Frequently asked questions

Can the H100 PCIe run Qwen2.5 14B Instruct?
The model needs about 11.2 GB and fits in the 77.6 GB available, with 66.4 GB to spare.
Which quantization should I use on the H100 PCIe?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~17.8 GB / 77.6 GB.
What is the largest context on the H100 PCIe?
At Q4_K_M, about 128K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Qwen2.5 14B Instruct (H100 PCIe)?
We estimate 109–169 tok/s (estimate, confidence low). Based on: memory bandwidth (2,000 GB/s) and model size.