Can Baichuan M2 32B Q4 K M run on Radeon Pro W6800 32GB?

YES — Tight Fit

C48Usable

Estimated from fit model

Baichuan M2 32B Q4 K M needs ~27.4 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: Balanced

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) — 27.4 GB, 14.7 tok/s, Tight fit

27.4 GB required32.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

14.7 tok/s

TTFT

13180 ms

Safe context

36K

Memory

27.4 GB / 32.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.8 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsBaichuan M2 32B Q4 K M on Radeon Pro W6800 32GB
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: 14.7 tok/s decode · 13.2s TTFT (warm) · 37 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	14.7 tok/s	7189 ms	36K
Coding	C	Tight fit	14.7 tok/s	13180 ms	36K
Agentic Coding	C	Runs with offload	14.7 tok/s	19171 ms	36K
Reasoning	C	Tight fit	14.7 tok/s	15577 ms	36K
RAG	C	Runs with offload	14.7 tok/s	23964 ms	36K

Quantization options

How Baichuan M2 32B Q4 K M (32B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	C47
Q3_K_S	3	15.7 GB	Low	C49
NVFP4	4

Get started

Copy-paste commands to run Baichuan M2 32B Q4 K M on your machine.

Run

lms load hf-baichuan-inc--baichuan-m2-32b-q4-k-m-gguf && lms server start

Upgrade options

Hardware that runs Baichuan M2 32B Q4 K M well

Radeon Pro W7900 48GBBudget pick

48 GB VRAM (+16)864 GB/s (+352)

Raises estimated decode speed by about 78%.26.1 tok/s decode

Raises estimated decode speed by about 78%.

Adds memory headroom for longer context windows and future model growth.

~$3,999 MSRP

Radeon PRO W7900 DS 48GBBest value

48 GB VRAM (+16)864 GB/s (+352)

Raises estimated decode speed by about 78%.26.1 tok/s decode

Raises estimated decode speed by about 78%.

Adds memory headroom for longer context windows and future model growth.

~$3,999 MSRP

AMD Instinct MI210 64GBAMD upgrade

64 GB VRAM (+32)1638 GB/s (+1126)

Raises estimated decode speed by about 288%.57.1 tok/s decode

Raises estimated decode speed by about 288%.

Adds memory headroom for longer context windows and future model growth.

~$10,000 MSRP

NVIDIA A100 40GBBiggest leap

40 GB VRAM (+8)1555 GB/s (+1043)

Raises estimated decode speed by about 355%.66.9 tok/s decode

Raises estimated decode speed by about 355%.

Adds memory headroom for longer context windows and future model growth.

~$10,000 MSRP

Frequently asked questions

Can Radeon Pro W6800 32GB run Baichuan M2 32B Q4 K M?

Yes, Radeon Pro W6800 32GB can run Baichuan M2 32B Q4 K M with a C grade (Tight fit). Expected decode speed: 14.7 tok/s.

How much VRAM does Baichuan M2 32B Q4 K M need?

Baichuan M2 32B Q4 K M (32B parameters) requires approximately 27.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Baichuan M2 32B Q4 K M?

The recommended quantization for Baichuan M2 32B Q4 K M is Q4_K_M, which balances quality and memory efficiency.

What speed will Baichuan M2 32B Q4 K M run at on Radeon Pro W6800 32GB?

On Radeon Pro W6800 32GB, Baichuan M2 32B Q4 K M achieves approximately 14.7 tokens per second decode speed with a time-to-first-token of 13180ms using Q4_K_M quantization.

Can Radeon Pro W6800 32GB run Baichuan M2 32B Q4 K M for coding?

For coding workloads, Baichuan M2 32B Q4 K M on Radeon Pro W6800 32GB receives a C grade with 14.7 tok/s and 36K context.

What context window can Baichuan M2 32B Q4 K M use on Radeon Pro W6800 32GB?

On Radeon Pro W6800 32GB, Baichuan M2 32B Q4 K M can safely use up to 36K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon Pro W6800 32GB See all hardware for Baichuan M2 32B Q4 K M

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