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URL: https://willitrunai.com/can-run/hf-mradermacher--baichuan-m3-235b-i1-gguf-on-b200-180gb

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Can Baichuan M3 235B i1 run on NVIDIA B200 180GB?

YES — With Offload

C41Usable
○Estimated from fit model

Baichuan M3 235B i1 needs ~189.8 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload
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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) — 189.8 GB, 36.6 tok/s, Runs with offload (needs ~7.4 GB host RAM)
189.8 GB required180.0 GB available
105% VRAM needed

9.8 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~7.4 GB host RAM)

Decode

36.6 tok/s

TTFT

5284 ms

Safe context

10K

Memory

189.8 GB / 180.0 GB

Offload

10%

Memory breakdown

Weights143.4 GB
KV Cache27.5 GB
Runtime0.9 GB
Headroom18.0 GB

See how fast it feels

See how fast it feelsBaichuan M3 235B i1 on NVIDIA B200 180GB
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: 36.6 tok/s decode · 5.3s TTFT (warm) · 92 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 7.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns with offload46.9 tok/s2253 ms10K
CodingCRuns with offload (needs ~7.4 GB host RAM)36.6 tok/s5284 ms10K
Agentic CodingFToo heavy29.3 tok/s9611 ms10K
ReasoningCRuns with offload (needs ~7.4 GB host RAM)36.6 tok/s6245 ms10K
RAGFToo heavy29.3 tok/s12014 ms

Quantization options

How Baichuan M3 235B i1 (235B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
91.7 GB
LowC47
Q3_K_S
3
115.2 GB
LowC47
NVFP4
4

Get started

Copy-paste commands to run Baichuan M3 235B i1 on your machine.

Run

lms load hf-mradermacher--baichuan-m3-235b-i1-gguf && lms server start

Upgrade options

Hardware that runs Baichuan M3 235B i1 well

👁 NVIDIA
B100 192GBBudget pick
192 GB VRAM (+12)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.46.9 tok/s decode

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

Raises estimated decode speed by about 28%.

~$35,000 MSRP

👁 NVIDIA
NVIDIA GB200 192GBBest value
192 GB VRAM (+12)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.46.9 tok/s decode

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

Raises estimated decode speed by about 28%.

~$60,000 MSRP

👁 NVIDIA
H100 NVL 188GBNVIDIA upgrade
188 GB VRAM (+8)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.36.8 tok/s decode

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 B200 180GBSee all hardware for Baichuan M3 235B i1
10K
131.6 GB
Medium
C47
Q4_K_MBest for your GPU
4
143.4 GB
MediumC47
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

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.