willitrun·ai

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 ms10K

Inference speed

Baichuan M3 235B i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Baichuan M3 235B i1 at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~3 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / MacMemoryQuantSpeed (tok/s)Fits?
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M3.4Heavy offload
NVIDIARTX 5090 32GB
32 GBQ4_K_M2.0Too big
NVIDIARTX 4090 24GB
24 GBQ4_K_M2.0Too big
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M2.0Too big
NVIDIARTX 3090 24GB
24 GBQ4_K_M2.0Too big
NVIDIARTX 4070 12GB
12 GBQ4_K_M2.0Too big
NVIDIARTX 3060 12GB
12 GBQ4_K_M2.0Too big
NVIDIARTX 4060 8GB
8 GBQ4_K_M2.0Too big
RX 7900 XTX 24GB
24 GBQ4_K_M2.0Too big
MacBook Pro M4 Max 128GB
128 GBQ4_K_M2.0Too big
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M2.0Too big
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M2.0Too big
MacBook Pro M4 Max 64GB
64 GBQ4_K_M2.0Too big
MacBook Pro M3 Max 64GB
64 GBQ4_K_M2.0Too big
MacBook Pro M1 Max 64GB
64 GBQ4_K_M2.0Too big
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M2.0Too big
NVIDIA2× RTX 4090 24GB
48 GBQ4_K_M2.0Too big
NVIDIA2× RTX 3090 24GB
48 GBQ4_K_M2.0Too big
2× RX 7900 XTX 24GB
48 GBQ4_K_M2.0Too big
NVIDIA4× RTX 3060 12GB
48 GBQ4_K_M2.0Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

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
131.6 GB
MediumC47
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

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-Optionen

Hardware, die Baichuan M3 235B i1 gut ausführt

NVIDIAB100 192GBGünstige Wahl
192 GB VRAM (+12)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.46.9 tok/s Dekodierung

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

Raises estimated decode speed by about 28%.

ca. $35,000 MSRP

NVIDIANVIDIA GB200 192GBBestes Preis-Leistungs-Verhältnis
192 GB VRAM (+12)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.46.9 tok/s Dekodierung

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

Raises estimated decode speed by about 28%.

ca. $60,000 MSRP

NVIDIAH100 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 Dekodierung

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

ca. $60,000 MSRP

Frequently asked questions

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

Yes, NVIDIA B200 180GB can run Baichuan M3 235B i1 with a C grade (Runs with offload (needs ~7.4 GB host RAM)). Expected decode speed: 36.6 tok/s.

How much VRAM does Baichuan M3 235B i1 need?

Baichuan M3 235B i1 (235B parameters) requires approximately 189.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Baichuan M3 235B i1?

The recommended quantization for Baichuan M3 235B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Baichuan M3 235B i1 run at on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Baichuan M3 235B i1 achieves approximately 36.6 tokens per second decode speed with a time-to-first-token of 5284ms using Q4_K_M quantization.

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

For coding workloads, Baichuan M3 235B i1 on NVIDIA B200 180GB receives a C grade with 36.6 tok/s and 10K context.

What context window can Baichuan M3 235B i1 use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Baichuan M3 235B i1 can safely use up to 10K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Baichuan M3 235B i1 feels slow on NVIDIA B200 180GB?

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.

See all results for NVIDIA B200 180GBSee all hardware for Baichuan M3 235B i1
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