HARDWARE FIT CHECK • Qwen 2.5 Coder 7B Instruct + NVIDIA H100 SXM (80GB HBM3 Cloud GPU)6.41 GB / 80 GB VRAM

Can I Run Qwen 2.5 Coder 7B Instruct on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)?

Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Qwen 2.5 Coder 7B Instruct (7.6B) on NVIDIA H100 SXM (80GB HBM3 Cloud GPU) (80 GB).

✓ FITS IN VRAM6.41 GB (8%)~503 t/s

Qwen 2.5 Coder 7B Instruct on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)

✓ Fits Comfortably in VRAM

✓ FITS IN VRAM

Required VRAM

6.41 GB

8% of hardware capacity (80 GB)

Est. Decode Speed

~503 tok/s

Dense model @ 3350 GB/s

Available Headroom

+73.59 GB

Safe for OS display & full context

Memory Allocation & Headroom

6.41 GB allocated of 80 GB capacity

8% utilized

Weights: 4.56 GB

KV: (32K): 0.94 GB

Overhead: 0.91 GB

Free Headroom: +73.59 GB

Architecture & Throughput Transparency: Single-user decode speed (~503 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA H100 SXM (80GB HBM3 Cloud GPU) (3350 GB/s). Prompt prefill / TTFT (~7,294 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).

Local Hardware Configurations & Pricing

High-bandwidth VRAM specs and current market prices

Consumer GPU

32 GB VRAM

NVIDIA GeForce RTX 5090 (32GB GDDR7)

Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization

1792 GB/s575W TDP

$1,999Amazon Prime →

Consumer GPU

24 GB VRAM

NVIDIA GeForce RTX 4090 (24GB GDDR6X)

The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models

1008 GB/s450W TDP

$1,699Amazon Prime →

Multi-GPU Rig

48 GB VRAM

2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)

Best price-to-VRAM homelab rig for Llama 3.3 70B Q4_K_M & Qwen 2.5 Coder 32B Q8_0

1648 GB/s700W TDP

$1,550Amazon Prime →

Datacenter Cloud GPU

80 GB VRAM

NVIDIA H100 SXM (80GB HBM3 Cloud GPU)

Production vLLM high-concurrency serving & 70B FP8 inference

3350 GB/s700W TDP

$28,000Amazon Prime →

Cloud Instance Options for Qwen 2.5 Coder 7B Instruct

On-Demand

Deploy this model on an on-demand cloud GPU without local hardware constraints or power usage:

RunPod Cloud GPUsBest 1-Click Ollama & vLLM

$0.38/hr (RTX 4090 Spot) – $2.39/hr (H100)

Launch 1-Click GPU Pod →

Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090

$0.18/hr (RTX 3060) – $0.78/hr (2x RTX 3090)

Rent Spot GPU →

Lambda GPU CloudEnterprise 8x H100 Clusters

$2.49/hr (H100) – $19.92/hr (8x H100)

Check H100 Availability →

Quantization Precision Matrix

32K ctx • KV: Q8_0

Quantization	Weights	KV Cache	Total VRAM	Est. Speed	Fit Status
FP1616 bpw	15.2 GB	0.94 GB	17.42 GB (22%)	~156 t/s	FITS
Q8_08.5 bpw	8.06 GB	0.94 GB	10.03 GB (13%)	~291 t/s	FITS
Q6_K6.56 bpw	6.23 GB	0.94 GB	8.14 GB (10%)	~373 t/s	FITS
Q5_K_M5.68 bpw	5.4 GB	0.94 GB	7.28 GB (9%)	~428 t/s	FITS
Q4_K_M4.8 bpw	4.56 GB	0.94 GB	6.41 GB (8%)	~503 t/s	FITS
FP44.25 bpw	4.03 GB	0.94 GB	5.86 GB (7%)	~566 t/s	FITS
IQ3_XS3.3 bpw	3.12 GB	0.94 GB	4.92 GB (6%)	~719 t/s	FITS

FAQ & Reference

Frequently Asked Questions: Qwen 2.5 Coder 7B Instruct on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)

How much VRAM does Qwen 2.5 Coder 7B Instruct need on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)?

At standard 4-bit quantization (Q4_K_M) with 32K context, Qwen 2.5 Coder 7B Instruct requires 6.41 GB of total VRAM (weights + KV cache + CUDA runtime overhead).

What inference speed will Qwen 2.5 Coder 7B Instruct achieve on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)?

Based on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)'s 3350 GB/s memory bandwidth, expected decode speed is approximately 503 tokens per second.

Can I expand context beyond 32K tokens?

Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.