BaichuanMed OCR 72B i1 needs ~66.6 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~4 tok/s.
Operating mode
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.
Select quantization to explore
Fit status
Runs well
Decode
3.7 tok/s
TTFT
51910 ms
Safe context
96K
Memory
66.6 GB / 108.8 GB
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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 3.7 tok/s | 28315 ms | 96K |
| Coding | C | Runs well | 3.7 tok/s | 51910 ms | 96K |
| Agentic Coding | C | Runs well | 3.7 tok/s | 75506 ms | 96K |
| Reasoning | C | Runs well | 3.7 tok/s | 61349 ms | 96K |
| RAG | C | Runs well | 3.7 tok/s | 94382 ms | 96K |
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 |
Copy-paste commands to run BaichuanMed OCR 72B i1 on your machine.
Run
lms load hf-mradermacher--baichuanmed-ocr-72b-i1-gguf && lms server startUpgrade options
NVIDIA H200 141GBBudget pickRaises estimated decode speed by about 2381%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBBest valueRaises estimated decode speed by about 2381%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA B200 180GBNVIDIA upgradeRaises estimated decode speed by about 4035%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run BaichuanMed OCR 72B i1 with a C grade (Runs well). Expected decode speed: 3.7 tok/s.
BaichuanMed OCR 72B i1 (72B parameters) requires approximately 66.6 GB of memory with Q4_K_M quantization.
The recommended quantization for BaichuanMed OCR 72B i1 is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA DGX Spark 128GB, BaichuanMed OCR 72B i1 achieves approximately 3.7 tokens per second decode speed with a time-to-first-token of 51910ms using Q4_K_M quantization.
For coding workloads, BaichuanMed OCR 72B i1 on NVIDIA DGX Spark 128GB receives a C grade with 3.7 tok/s and 96K context.
On NVIDIA DGX Spark 128GB, BaichuanMed OCR 72B i1 can safely use up to 96K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--baichuanmed-ocr-72b-i1-gguf-on-dgx-spark-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
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 |
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.
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.