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HARDWARE FIT CHECK • DeepSeek-V3 (671B MoE) + NVIDIA H100 SXM (80GB HBM3 Cloud GPU)482.94 GB / 80 GB VRAM

Can I Run DeepSeek-V3 (671B 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-V3 (671B MoE) (671B) on NVIDIA H100 SXM (80GB HBM3 Cloud GPU) (80 GB).

✕ OOM482.94 GB (604%)0 t/s

Target Hardware & Model

interactive
Ultra-fast MoE generation matching closed frontier models at fraction of hardware cost
DeepSeek-V3 (671B MoE) on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)MoE (37B Active / 671B Total)

✕ Out of Memory — Exceeds VRAM Capacity

✕ OOM
Required VRAM
482.94 GB
604% of hardware capacity (80 GB)
Est. Decode Speed
0 tok/s
37B active params @ 3350 GB/s
Available Headroom
-402.94 GB
Needs +402.94 GB more VRAM
Memory Allocation & Headroom
482.94 GB allocated of 80 GB capacity
604% utilized
Weights: 402.6 GBMoE (37B Active)
KV: (32K): 65.5 GBMLA (-85%)
Overhead: 14.84 GB
Deficit: -402.94 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).
MoE Memory vs Speed Architecture: While decode throughput scales with the active parameter count (37B parameters streamed per token), all 671B parameters must be resident in VRAM so the dynamic router can route tokens to any expert layer.
Multi-Head Latent Attention (MLA): DeepSeek compresses Key-Value projections into a low-rank latent vector, cutting KV memory footprint by ~85% compared to standard MHA/GQA.

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-V3 (671B MoE) @ 482.94 GB VRAM)

RunPod Cloud GPUsBest 1-Click Ollama & vLLM
$0.34/hr (RTX 4070 Ti) – $2.39/hr (H100)
Launch 1-Click GPU Pod →
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
QuantizationWeightsKV CacheTotal VRAMEst. SpeedFit Status
FP1616 bpw1342 GB65.5 GB1455.22 GB (999%)0 t/sOOM
Q8_08.5 bpw711.26 GB65.5 GB802.4 GB (999%)0 t/sOOM
Q6_K6.56 bpw550.22 GB65.5 GB635.73 GB (795%)0 t/sOOM
Q5_K_M5.68 bpw476.41 GB65.5 GB559.33 GB (699%)0 t/sOOM
Q4_K_M4.8 bpw402.6 GB65.5 GB482.94 GB (604%)0 t/sOOM
FP44.25 bpw355.63 GB65.5 GB434.33 GB (543%)0 t/sOOM
IQ3_XS3.3 bpw275.11 GB65.5 GB350.99 GB (439%)0 t/sOOM

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FAQ & Reference

Frequently Asked Questions: DeepSeek-V3 (671B MoE) on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)

How much VRAM does DeepSeek-V3 (671B MoE) need on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)?

At standard 4-bit quantization (Q4_K_M) with 32K context, DeepSeek-V3 (671B MoE) requires 482.94 GB of total VRAM (weights + KV cache + CUDA runtime overhead).

What inference speed will DeepSeek-V3 (671B 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.