HARDWARE FIT CHECK โข Qwen 3.6 35B-A3B MoE + NVIDIA H100 SXM (80GB HBM3 Cloud GPU)24.1 GB / 80 GB VRAM
Can I Run Qwen 3.6 35B-A3B 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 Qwen 3.6 35B-A3B MoE (35B) on NVIDIA H100 SXM (80GB HBM3 Cloud GPU) (80 GB).
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Runs 100% in GPU VRAM (Optimal Headroom)24.1 GB (30%)~1095 t/s
Qwen 3.6 35B-A3B MoE on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)
โ Fits Comfortably in VRAM
โ FITS IN VRAM
Required VRAM
24.1 GB
30% of hardware capacity (80 GB)Est. Decode Speed
~1095 tok/s
3B active params @ 3350 GB/sAvailable Headroom
+55.9 GB
Safe for OS display & full contextMemory Allocation & Headroom
24.1 GB allocated of 80 GB capacity
30% utilized
Weights: 21 GB
KV: (32K): 1.61 GB
Overhead: 1.49 GB
Free Headroom: +55.9 GB
โก Architecture & Throughput Transparency: Single-user decode speed (~1095 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA H100 SXM (80GB HBM3 Cloud GPU) (3350 GB/s). Prompt prefill / TTFT (~15,878 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 qwen3.6:35b-a3bCloud Instance Options for Qwen 3.6 35B-A3B MoE
On-DemandDeploy this model on an on-demand cloud GPU without local hardware constraints or power usage:
RunPod Cloud GPUsBest 1-Click Ollama & vLLM
$0.34/hr (RTX 4070 Ti) โ $2.39/hr (H100)Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090
$0.18/hr (RTX 3060) โ $0.55/hr (RTX 4090)Lambda GPU CloudEnterprise 8x H100 Clusters
$1.99/hr (GH200 / H100)Quantization Precision Matrix
32K ctx โข KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 70 GB | 1.61 GB | 74.81 GB (94%) | ~377 t/s | FITS |
| Q8_08.5 bpw | 37.1 GB | 1.61 GB | 40.76 GB (51%) | ~673 t/s | FITS |
| Q6_K6.56 bpw | 28.7 GB | 1.61 GB | 32.06 GB (40%) | ~843 t/s | FITS |
| Q5_K_M5.68 bpw | 24.85 GB | 1.61 GB | 28.08 GB (35%) | ~952 t/s | FITS |
| Q4_K_M4.8 bpw | 21 GB | 1.61 GB | 24.1 GB (30%) | ~1095 t/s | FITS |
| FP44.25 bpw | 18.55 GB | 1.61 GB | 21.56 GB (27%) | ~1211 t/s | FITS |
| IQ3_XS3.3 bpw | 14.35 GB | 1.61 GB | 17.21 GB (22%) | ~1477 t/s | FITS |
Local Hardware Configurations & Pricing
High-bandwidth VRAM specs and current market pricesDatacenter Cloud GPU80 GB VRAM
NVIDIA H100 SXM (80GB HBM3 Cloud GPU)
Production vLLM high-concurrency serving & 70B FP8 inference
3350 GB/s700W TDP
$28,000Amazon Prime โ
Consumer GPU32 GB VRAM
NVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27Bโ35B models & Blackwell FP4 quantization
1792 GB/s575W TDP
$1,999Amazon Prime โ
Consumer GPU24 GB VRAM
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14Bโ32B Q4_K_M models
1008 GB/s450W TDP
$1,699Amazon Prime โ
Multi-GPU Rig48 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 โ
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