Will It Run AI

Can BaichuanMed OCR 72B i1 run on AMD Instinct MI100 32GB?

NO — Won't Fit

F0Won't run
Estimated from fit model

BaichuanMed OCR 72B i1 needs ~56.5 GB but AMD Instinct MI100 32GB only has 32.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: HighStack: StandardBottleneck: Memory capacity
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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) 56.5 GB, exceeds 32.0 GB available
56.5 GB required32.0 GB available
177% VRAM needed

24.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.1 tok/s

TTFT

46928 ms

Safe context

4K

Memory

56.5 GB / 32.0 GB

Offload

40%

Memory breakdown

Weights43.9 GB
KV Cache8.4 GB
Runtime0.9 GB
Headroom3.2 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsBaichuanMed OCR 72B i1 on AMD Instinct MI100 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: 4.1 tok/s decode · 46.9s TTFT (warm) · 10 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 56.5 GB, but this setup only exposes 32.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy4.9 tok/s21736 ms4K
CodingFToo heavy4.1 tok/s46928 ms4K
Agentic CodingFToo heavy3.1 tok/s91514 ms4K
ReasoningFToo heavy4.1 tok/s55460 ms4K
RAGFToo heavy3.1 tok/s114392 ms4K

Quantization options

How BaichuanMed OCR 72B i1 (72B params) fits at each quantization level on AMD Instinct MI100 32GB (32.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
28.1 GB
LowF0
Q3_K_S
3
35.3 GB
LowF0
NVFP4
4
40.3 GB
MediumF0
Q4_K_M
4
43.9 GB
MediumF0
Q5_K_M
5
51.8 GB
HighF0
Q6_K
6
59.0 GB
HighF0
Q8_0
8
77.0 GB
Very HighF0
F16
16
147.6 GB
MaximumF0

Opciones de mejora

Hardware que ejecuta bien BaichuanMed OCR 72B i1

Frequently asked questions

Can AMD Instinct MI100 32GB run BaichuanMed OCR 72B i1?

No, BaichuanMed OCR 72B i1 requires more memory than AMD Instinct MI100 32GB provides.

How much VRAM does BaichuanMed OCR 72B i1 need?

BaichuanMed OCR 72B i1 (72B parameters) requires approximately 56.5 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 MI100 32GB?

On AMD Instinct MI100 32GB, BaichuanMed OCR 72B i1 achieves approximately 4.1 tokens per second decode speed with a time-to-first-token of 46928ms using Q4_K_M quantization.

Can AMD Instinct MI100 32GB run BaichuanMed OCR 72B i1 for coding?

For coding workloads, BaichuanMed OCR 72B i1 on AMD Instinct MI100 32GB receives a F grade with 4.1 tok/s and 4K context.

What context window can BaichuanMed OCR 72B i1 use on AMD Instinct MI100 32GB?

On AMD Instinct MI100 32GB, BaichuanMed OCR 72B i1 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if BaichuanMed OCR 72B i1 feels slow on AMD Instinct MI100 32GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for AMD Instinct MI100 32GBSee all hardware for BaichuanMed OCR 72B i1
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