Can BaichuanMed OCR 72B i1 run on AMD Instinct MI350X 288GB?

YES — Runs Great

C47Usable

Estimated from fit model

BaichuanMed OCR 72B i1 needs ~82.1 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q4_K_M quantization, expect ~133 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 82.1 GB, 133.0 tok/s, Runs well

82.1 GB required288.0 GB available

29% VRAM used

Fit status

Runs well

Decode

133.0 tok/s

TTFT

1456 ms

Safe context

407K

Memory

82.1 GB / 288.0 GB

Memory breakdown

Weights43.9 GB

KV Cache8.4 GB

Runtime0.9 GB

Headroom28.8 GB

See how fast it feels

See how fast it feelsBaichuanMed OCR 72B i1 on AMD Instinct MI350X 288GB
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: 133.0 tok/s decode · 1.5s TTFT (warm) · 332 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	133.0 tok/s	794 ms	407K
Coding	C	Runs well	133.0 tok/s	1456 ms	407K
Agentic Coding	C	Runs well	133.0 tok/s	2118 ms	407K
Reasoning	C	Runs well	133.0 tok/s	1721 ms	407K
RAG	C	Runs well	133.0 tok/s	2647 ms	407K

Inference speed

BaichuanMed OCR 72B i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for BaichuanMed OCR 72B i1 at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~14 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 / Mac	Memory	Quant	Speed (tok/s)	Fits?
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	13.7	Fits
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	13.4	Too big
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	12.7	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	10.6	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	10.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	9.4	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	7.1	Too big
RTX 5090 32GB	32 GB	Q4_K_M	6.5	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	6.5	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	5.7	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	4.4	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	3.8	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	3.4	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.6	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.4	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.2	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.1	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too 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 BaichuanMed OCR 72B i1 (72B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	28.1 GB	Low	D37
Q3_K_S	3	35.3 GB	Low	D37
NVFP4	4	40.3 GB	Medium	D38
Q4_K_M	4	43.9 GB	Medium	D38
Q5_K_M	5	51.8 GB	High	D39
Q6_K	6	59.0 GB	High	D39
Q8_0	8	77.0 GB	Very High	C41
F16Best for your GPU	16	147.6 GB	Maximum	C46

Get started

Copy-paste commands to run BaichuanMed OCR 72B i1 on your machine.

Run

lms load hf-mradermacher--baichuanmed-ocr-72b-i1-gguf && lms server start

Frequently asked questions

Can AMD Instinct MI350X 288GB run BaichuanMed OCR 72B i1?

Yes, AMD Instinct MI350X 288GB can run BaichuanMed OCR 72B i1 with a C grade (Runs well). Expected decode speed: 133.0 tok/s.

How much VRAM does BaichuanMed OCR 72B i1 need?

BaichuanMed OCR 72B i1 (72B parameters) requires approximately 82.1 GB of memory with Q4_K_M quantization.

What is the best quantization for BaichuanMed OCR 72B i1?

The recommended quantization for BaichuanMed OCR 72B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will BaichuanMed OCR 72B i1 run at on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, BaichuanMed OCR 72B i1 achieves approximately 133.0 tokens per second decode speed with a time-to-first-token of 1456ms using Q4_K_M quantization.

Can AMD Instinct MI350X 288GB run BaichuanMed OCR 72B i1 for coding?

For coding workloads, BaichuanMed OCR 72B i1 on AMD Instinct MI350X 288GB receives a C grade with 133.0 tok/s and 407K context.

What context window can BaichuanMed OCR 72B i1 use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, BaichuanMed OCR 72B i1 can safely use up to 407K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for AMD Instinct MI350X 288GB See all hardware for BaichuanMed OCR 72B i1

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