Can BaichuanMed OCR 72B i1 run on NVIDIA DGX Spark 128GB?

YES — With Q8_0

C42Usable

Estimated from fit model

BaichuanMed OCR 72B i1 needs ~99.7 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With Q8_0 quantization, expect ~2 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Memory bandwidth

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.

BaichuanMed OCR 72B i1 at Q4_K_M needs 53.6 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at Q8_0 (99.7 GB) with very high quality. 7 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 66.6 GB, 3.7 tok/s, Runs well

66.6 GB required108.8 GB available

61% VRAM used

Fit status

Runs well

Decode

3.7 tok/s

TTFT

51910 ms

Safe context

96K

Memory

66.6 GB / 108.8 GB

Memory breakdown

Weights43.9 GB

KV Cache8.4 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsBaichuanMed OCR 72B i1 on NVIDIA DGX Spark 128GB
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: 3.7 tok/s decode · 51.9s TTFT (warm) · 9 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Quantization options

How BaichuanMed OCR 72B i1 (72B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	28.1 GB	Low	C42
Q3_K_S	3	35.3 GB	Low	C44
NVFP4	4	40.3 GB	Medium	C45
Q4_K_M	4	43.9 GB	Medium	C46
Q5_K_M	5	51.8 GB	High	C47
Q6_K	6	59.0 GB	High	C47
Q8_0Best for your GPU	8	77.0 GB	Very High	C47
F16	16	147.6 GB	Maximum	F0

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

升级选项

能流畅运行 BaichuanMed OCR 72B i1 的硬件

NVIDIA H200 141GB经济之选

141 GB VRAM (+13)4800 GB/s (+4527)

Raises estimated decode speed by about 2381%.91.8 tok/s 解码

Raises estimated decode speed by about 2381%.

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

~$30,000 MSRP

NVIDIA H200 PCIe 141GB最佳性价比

141 GB VRAM (+13)4800 GB/s (+4527)

Raises estimated decode speed by about 2381%.91.8 tok/s 解码

Raises estimated decode speed by about 2381%.

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

~$30,000 MSRP

NVIDIA B200 180GBNVIDIA 升级

180 GB VRAM (+52)8000 GB/s (+7727)

Raises estimated decode speed by about 4035%.153 tok/s 解码

Raises estimated decode speed by about 4035%.

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

~$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run BaichuanMed OCR 72B i1?

Yes, NVIDIA DGX Spark 128GB can run BaichuanMed OCR 72B i1 at Q8_0 quantization (Tight fit). The recommended Q4_K_M requires 53.6 GB which exceeds available memory, but at Q8_0 it needs only 99.7 GB. Expected decode speed: 2.3 tok/s.

How much VRAM does BaichuanMed OCR 72B i1 need?

BaichuanMed OCR 72B i1 (72B parameters) requires approximately 53.6 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at Q8_0 using 99.7 GB.

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

The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is Q8_0, which uses 99.7 GB.

What speed will BaichuanMed OCR 72B i1 run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, BaichuanMed OCR 72B i1 achieves approximately 2.3 tokens per second decode speed with a time-to-first-token of 82778ms using Q8_0 quantization.

Can NVIDIA DGX Spark 128GB run BaichuanMed OCR 72B i1 for coding?

For coding workloads, BaichuanMed OCR 72B i1 on NVIDIA DGX Spark 128GB receives a F grade with 2.0 tok/s and 4K context.

What context window can BaichuanMed OCR 72B i1 use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, BaichuanMed OCR 72B i1 can safely use up to 33K tokens of context at Q8_0 quantization. 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 NVIDIA DGX Spark 128GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for BaichuanMed OCR 72B i1?

Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for NVIDIA DGX Spark 128GB See all hardware for BaichuanMed OCR 72B i1

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