HARDWARE BENCHMARK & VRAM ENVELOPE โ€ข DATACENTER CLOUD GPU80 GB VRAM โ€ข 3350 GB/s

What Models Can Run on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)?

80 GB VRAM3350 GB/s Bandwidth700W TDPMSRP: $28,000Cloud: $2.39/hr on RunPod

NVIDIA H100 SXM (80GB HBM3 Cloud GPU) provides 80 GB of usable memory with 3350 GB/s memory bandwidth at 700W TDP. Production vLLM high-concurrency serving & 70B FP8 inference.

โœ… Runs 100% in GPU VRAM (Optimal Headroom)21.93 GB (27%)~139 t/s

Model & Target Hardware

interactive
Agentic Coding, Terminal Workflows & Multi-File Refactoring
Qwen 3.8 27B Instruct on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)

โœ“ Fits Comfortably in VRAM

โœ“ FITS IN VRAM
Required VRAM
21.93 GB
27% of hardware capacity (80 GB)
Est. Decode Speed
~139 tok/s
Dense model @ 3350 GB/s
Available Headroom
+58.07 GB
Safe for OS display & full context
Memory Allocation & Headroom
21.93 GB allocated of 80 GB capacity
27% utilized
Weights: 16.32 GB
KV: (32K): 4.29 GB
Overhead: 1.32 GB
Free Headroom: +58.07 GB
โšก Architecture & Throughput Transparency: Single-user decode speed (~139 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA H100 SXM (80GB HBM3 Cloud GPU) (3350 GB/s). Prompt prefill / TTFT (~2,016 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 qwen3.8:27b

Quantization Precision Matrix

32K ctx โ€ข KV: Q8_0
QuantizationWeightsKV CacheTotal VRAMEst. SpeedFit Status
FP1616 bpw54.4 GB4.29 GB61.34 GB (77%)~44 t/sFITS
Q8_08.5 bpw28.83 GB4.29 GB34.88 GB (44%)~81 t/sFITS
Q6_K6.56 bpw22.3 GB4.29 GB28.12 GB (35%)~104 t/sFITS
Q5_K_M5.68 bpw19.31 GB4.29 GB25.03 GB (31%)~119 t/sFITS
Q4_K_M4.8 bpw16.32 GB4.29 GB21.93 GB (27%)~139 t/sFITS
FP44.25 bpw14.42 GB4.29 GB19.96 GB (25%)~156 t/sFITS
IQ3_XS3.3 bpw11.15 GB4.29 GB16.58 GB (21%)~199 t/sFITS

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All Open-Weights Models Tested on NVIDIA H100 SXM (80GB HBM3 Cloud GPU) (Q4_K_M @ 32K Context)

Standard Q4_K_M precision with 32K context window and Q8_0 KV cache

12 Models Benchmarked
ModelArchitectureTotal VRAM NeededHeadroomEst. Decode SpeedCompatibilityDetailed Config
Qwen 3.8 27B Instruct
Alibaba Cloud
27.2B Dense21.93 GB(27%)+58.07 GB~139 tok/sโœ“ FITSConfigure CLI โ†’
Qwen 3.6 35B-A3B MoE
Alibaba Cloud
35B MoE24.1 GB(30%)+55.9 GB~1095 tok/sโœ“ FITSConfigure CLI โ†’
DeepSeek-V4.1-Flash (552B Engram MoE)
DeepSeek
552B MoE345.59 GB(432%)-265.59 GB0 (OOM)โœ• OOMConfigure CLI โ†’
Meta Muse Spark Open (816B-A42B MoE)
Meta AI
816B MoE512.86 GB(641%)-432.86 GB0 (OOM)โœ• OOMConfigure CLI โ†’
Google Gemma 4 26B MoE
Google
26.4B MoE20.23 GB(25%)+59.77 GB~733 tok/sโœ“ FITSConfigure CLI โ†’
Google Gemma 4 E4B (Edge 4B)
Google
4.1B Dense4.24 GB(5%)+75.76 GB~895 tok/sโœ“ FITSConfigure CLI โ†’
DeepSeek-R1 Distill Qwen 32B
DeepSeek
32.8B Dense25.41 GB(32%)+54.59 GB~117 tok/sโœ“ FITSConfigure CLI โ†’
DeepSeek-R1 Distill Qwen 14B
DeepSeek
14.8B Dense13.16 GB(16%)+66.84 GB~249 tok/sโœ“ FITSConfigure CLI โ†’
Meta Llama 4 Scout (109B-A17B MoE)
Meta AI
109B MoE71.66 GB(90%)+8.34 GB~219 tok/sโšก TIGHT FITConfigure CLI โ†’
Meta Llama 3.3 70B Instruct
Meta AI
70.6B Dense49.96 GB(62%)+30.04 GB~56 tok/sโœ“ FITSConfigure CLI โ†’
Meta Llama 3.1 8B Instruct
Meta AI
8B Dense7.87 GB(10%)+72.13 GB~452 tok/sโœ“ FITSConfigure CLI โ†’
Microsoft Phi-4 Mini 3.8B
Microsoft
3.8B Dense3.92 GB(5%)+76.08 GB~972 tok/sโœ“ FITSConfigure CLI โ†’