Will It Run AI

Can baichuan inc Baichuan M2 32B run on AMD Instinct MI60 32GB?

YES — Tight Fit

C49Usable
Estimated from fit model

baichuan inc Baichuan M2 32B needs ~27.4 GB VRAM. AMD Instinct MI60 32GB has 32.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: HighStack: StandardBottleneck: Balanced
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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) — 27.4 GB, 25.7 tok/s, Tight fit
27.4 GB required32.0 GB available
86% VRAM used

Fit status

Tight fit

Decode

25.7 tok/s

TTFT

7532 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 inc Baichuan M2 32B on AMD Instinct MI60 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: 25.7 tok/s decode · 7.5s TTFT (warm) · 64 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well25.7 tok/s4108 ms36K
CodingCTight fit25.7 tok/s7532 ms36K
Agentic CodingCRuns with offload25.7 tok/s10955 ms36K
ReasoningCTight fit25.7 tok/s8901 ms36K
RAGCRuns with offload25.7 tok/s13694 ms36K

Quantization options

How baichuan inc Baichuan M2 32B (32B params) fits at each quantization level on AMD Instinct MI60 32GB (32.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowC47
Q3_K_S
3
15.7 GB
LowC49
NVFP4
4
17.9 GB
MediumC49
Q4_K_M
4
19.5 GB
MediumC49
Q5_K_MBest for your GPU
5
23.0 GB
HighC48
Q6_K
6
26.2 GB
HighF0
Q8_0
8
34.2 GB
Very HighF0
F16
16
65.6 GB
MaximumF0

Get started

Copy-paste commands to run baichuan inc Baichuan M2 32B on your machine.

Run

lms load hf-bartowski--baichuan-inc-baichuan-m2-32b-gguf && lms server start

Opções de upgrade

Hardware que roda bem baichuan inc Baichuan M2 32B

Radeon Pro W7900 48GBOpção econômica
48 GB VRAM (+16)
C
Adds memory headroom for longer context windows and future model growth.26.1 tok/s decodificação

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

~$3,999 MSRP

Radeon PRO W7900 DS 48GBMelhor custo-benefício
48 GB VRAM (+16)
C
Adds memory headroom for longer context windows and future model growth.26.1 tok/s decodificação

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

~$3,999 MSRP

AMD Instinct MI210 64GBUpgrade AMD
64 GB VRAM (+32)1638 GB/s (+614)
C
Raises estimated decode speed by about 122%.57.1 tok/s decodificação

Raises estimated decode speed by about 122%.

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

~$10,000 MSRP

NVIDIANVIDIA A100 40GBMaior salto
40 GB VRAM (+8)1555 GB/s (+531)
B
Raises estimated decode speed by about 160%.66.9 tok/s decodificação

Raises estimated decode speed by about 160%.

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

~$10,000 MSRP

Frequently asked questions

Can AMD Instinct MI60 32GB run baichuan inc Baichuan M2 32B?

Yes, AMD Instinct MI60 32GB can run baichuan inc Baichuan M2 32B with a C grade (Tight fit). Expected decode speed: 25.7 tok/s.

How much VRAM does baichuan inc Baichuan M2 32B need?

baichuan inc Baichuan M2 32B (32B parameters) requires approximately 27.4 GB of memory with Q4_K_M quantization.

What is the best quantization for baichuan inc Baichuan M2 32B?

The recommended quantization for baichuan inc Baichuan M2 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will baichuan inc Baichuan M2 32B run at on AMD Instinct MI60 32GB?

On AMD Instinct MI60 32GB, baichuan inc Baichuan M2 32B achieves approximately 25.7 tokens per second decode speed with a time-to-first-token of 7532ms using Q4_K_M quantization.

Can AMD Instinct MI60 32GB run baichuan inc Baichuan M2 32B for coding?

For coding workloads, baichuan inc Baichuan M2 32B on AMD Instinct MI60 32GB receives a C grade with 25.7 tok/s and 36K context.

What context window can baichuan inc Baichuan M2 32B use on AMD Instinct MI60 32GB?

On AMD Instinct MI60 32GB, baichuan inc Baichuan M2 32B 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 AMD Instinct MI60 32GBSee all hardware for baichuan inc Baichuan M2 32B
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