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URL: https://willitrunai.com/can-run/hf-mradermacher--baichuan-m3-235b-gguf-on-h100-80gb

⇱ Baichuan M3 235B on NVIDIA H100 80GB? No — Alternatives

Can Baichuan M3 235B run on NVIDIA H100 80GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

Baichuan M3 235B needs ~180.1 GB but NVIDIA H100 80GB only has 80.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: HighStack: BasicBottleneck: Memory capacity
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Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 180.1 GB, exceeds 80.0 GB available
180.1 GB required80.0 GB available
225% VRAM needed

100.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.4 tok/s

TTFT

44108 ms

Safe context

4K

Memory

180.1 GB / 80.0 GB

Offload

60%

Memory breakdown

Weights143.4 GB
KV Cache27.5 GB
Runtime1.2 GB
Headroom8.0 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsBaichuan M3 235B on NVIDIA H100 80GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API. When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed. Key benefits of running locally: - Full privacy: your prompts never leave your machine - No per-token cost or rate limits - Works offline once the model is downloaded - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb: Model size Q4 (4-bit) Q8 (8-bit) FP16 7B params ~4.3 GB ~7.5 GB ~14 GB 13B params ~7.9 GB ~13.9 GB ~26 GB 70B params ~42.7 GB ~74.9 GB ~140 GB Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 4.4 tok/s decode · 44.1s TTFT (warm) · 11 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 180.1 GB, but this setup only exposes 80.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy5.0 tok/s21100 ms4K
CodingFToo heavy4.4 tok/s44108 ms4K
Agentic CodingFToo heavy3.5 tok/s81135 ms4K
ReasoningFToo heavy4.4 tok/s52127 ms4K
RAGFToo heavy3.5 tok/s101419 ms4K

Quantization options

How Baichuan M3 235B (235B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
91.7 GB
LowF0
Q3_K_S
3
115.2 GB
LowF0
NVFP4
4
131.6 GB
MediumF0
Q4_K_M
4
143.4 GB
MediumF0
Q5_K_M
5
169.2 GB
HighF0
Q6_K
6
192.7 GB
HighF0
Q8_0
8
251.5 GB
Very HighF0
F16
16
481.7 GB
MaximumF0

Upgrade options

Hardware that runs Baichuan M3 235B well

👁 NVIDIA
NVIDIA B200 180GBBudget pick
180 GB VRAM (+100)8000 GB/s (+4650)
C
Makes the model fit on the accelerator instead of staying completely out of reach.36.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 730%.

~$30,000 MSRP

👁 NVIDIA
B100 192GBBest value
192 GB VRAM (+112)8000 GB/s (+4650)
C
Makes the model fit on the accelerator instead of staying completely out of reach.46.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$35,000 MSRP

👁 NVIDIA
NVIDIA GB200 192GBNVIDIA upgrade
192 GB VRAM (+112)8000 GB/s (+4650)
C
Makes the model fit on the accelerator instead of staying completely out of reach.46.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$60,000 MSRP

Frequently asked questions

See all results for NVIDIA H100 80GBSee all hardware for Baichuan M3 235B