HARDWARE FIT CHECK • DeepSeek Coder V2 236B MoE + NVIDIA H100 SXM (80GB HBM3 Cloud GPU)151.34 GB / 80 GB VRAM

Can I Run DeepSeek Coder V2 236B MoE 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 DeepSeek Coder V2 236B MoE (236B) on NVIDIA H100 SXM (80GB HBM3 Cloud GPU) (80 GB).

✕ OOM151.34 GB (189%)0 t/s

DeepSeek Coder V2 236B MoE on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)

✕ Out of Memory — Exceeds VRAM Capacity

✕ OOM

Required VRAM

151.34 GB

189% of hardware capacity (80 GB)

Est. Decode Speed

0 tok/s

16B active params @ 3350 GB/s

Available Headroom

-71.34 GB

Needs +71.34 GB more VRAM

Memory Allocation & Headroom

151.34 GB allocated of 80 GB capacity

189% utilized

Weights: 141.6 GB

KV: (32K): 4.03 GB

Overhead: 5.71 GB

Deficit: -71.34 GB

⚡ Architecture & Throughput Transparency: Single-user decode speed (~0 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA H100 SXM (80GB HBM3 Cloud GPU) (3350 GB/s). Prompt prefill / TTFT (~0 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).

OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run deepseek-coder-v2:236b

Local Hardware Configurations & Pricing

⚠️ Out of VRAM: High-Capacity Hardware Upgrades

High-bandwidth VRAM specs and current market prices

Multi-GPU Rig

48 GB VRAM

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

Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8

936 GB/s700W TDP

$1,550Amazon Prime →

Apple Silicon↑ Upgrade

112 GB VRAM

Apple Mac Studio M4 Max (128GB Unified Memory)

Running Llama 4 Scout 109B & 70B Q8 locally with 128K+ context

546 GB/s145W TDP

$3,499Amazon 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 →

⚠️ Out of VRAM? Don't downgrade model quality.

Out of VRAM

Spin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (DeepSeek Coder V2 236B MoE @ 151.34 GB VRAM)

RunPod Cloud GPUsBest 1-Click Ollama & vLLM

$0.34/hr (RTX 4070 Ti) – $2.39/hr (H100)

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Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090

$0.18/hr (RTX 3060) – $0.55/hr (RTX 4090)

Rent Spot GPU →

Lambda GPU CloudEnterprise 8x H100 Clusters

$1.99/hr (GH200 / H100)

Check H100 Availability →

Quantization Precision Matrix

32K ctx • KV: Q8_0

Quantization	Weights	KV Cache	Total VRAM	Est. Speed	Fit Status
FP1616 bpw	472 GB	4.03 GB	493.3 GB (617%)	0 t/s	OOM
Q8_08.5 bpw	250.16 GB	4.03 GB	263.7 GB (330%)	0 t/s	OOM
Q6_K6.56 bpw	193.52 GB	4.03 GB	205.07 GB (256%)	0 t/s	OOM
Q5_K_M5.68 bpw	167.56 GB	4.03 GB	178.2 GB (223%)	0 t/s	OOM
Q4_K_M4.8 bpw	141.6 GB	4.03 GB	151.34 GB (189%)	0 t/s	OOM
FP44.25 bpw	125.08 GB	4.03 GB	134.24 GB (168%)	0 t/s	OOM
IQ3_XS3.3 bpw	96.76 GB	4.03 GB	104.93 GB (131%)	~70 t/s	PARTIAL

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FAQ & Reference

Frequently Asked Questions: DeepSeek Coder V2 236B MoE on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)

How much VRAM does DeepSeek Coder V2 236B MoE need on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)?

At standard 4-bit quantization (Q4_K_M) with 32K context, DeepSeek Coder V2 236B MoE requires 151.34 GB of total VRAM (weights + KV cache + CUDA runtime overhead).

What inference speed will DeepSeek Coder V2 236B MoE 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 0 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.