NVIDIA H200 141GBコスパ重視141 GB VRAM (+13)4800 GB/s (+4527)
B
Raises estimated decode speed by about 917%.182 tok/sデコード
Raises estimated decode speed by about 917%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Can Baichuan 13B run on NVIDIA DGX Spark 128GB?
YES — Runs Great
B60Good
Estimated from fit model
Baichuan 13B needs ~35.8 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q5_K_M quantization, expect ~18 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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.
Select quantization to explore
Q5_K_M (High quality) — 35.8 GB, 17.9 tok/s, Runs well
35.8 GB required108.8 GB available
33% VRAM used
Fit status
Runs well
Decode
17.9 tok/s
TTFT
10846 ms
Safe context
8K
Memory
35.8 GB / 108.8 GB
Memory breakdown
Weights9.4 GB
KV Cache12.2 GB
Runtime1.2 GB
Headroom13.1 GB
See how fast it feels
See how fast it feelsBaichuan 13B on NVIDIA DGX Spark 128GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 17.9 tok/s decode · 10.8s TTFT (warm) · 45 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
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
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 17.9 tok/s | 5916 ms | 8K |
| Coding | B | Runs well | 17.9 tok/s | 10846 ms | 8K |
| Agentic Coding | B | Runs well | 17.9 tok/s | 15776 ms | 8K |
| Reasoning | B | Runs well | 17.9 tok/s | 12818 ms | 8K |
| RAG | B | Runs well | 17.9 tok/s | 19720 ms | 8K |
Quantization options
How Baichuan 13B (13B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B55 |
Q3_K_S | 3 | 6.4 GB | Low | B55 |
NVFP4 | 4 | 7.3 GB | Medium | B55 |
Q4_K_M | 4 | 7.9 GB | Medium | B56 |
Q5_K_M | 5 | 9.4 GB | High | B56 |
Q6_K | 6 | 10.7 GB | High | B56 |
Q8_0 | 8 | 13.9 GB | Very High | B56 |
F16Best for your GPU | 16 | 26.7 GB | Maximum | B58 |
Get started
Copy-paste commands to run Baichuan 13B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "baichuan-inc/Baichuan-13B-Chat" \
--hf-file "Baichuan-13B-Chat-Q5_K_M.gguf" \
-c 4096 -ngl 99アップグレードオプション
Baichuan 13Bを快適に動かすハードウェア
NVIDIA H200 141GBコスパ重視NVIDIA H200 PCIe 141GBコスパ最良141 GB VRAM (+13)4800 GB/s (+4527)
B
Raises estimated decode speed by about 917%.182 tok/sデコード
Raises estimated decode speed by about 917%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
NVIDIA B200 180GBNVIDIAアップグレード180 GB VRAM (+52)8000 GB/s (+7727)
B
Raises estimated decode speed by about 917%.182 tok/sデコード
Raises estimated decode speed by about 917%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Frequently asked questions
Can NVIDIA DGX Spark 128GB run Baichuan 13B?
Yes, NVIDIA DGX Spark 128GB can run Baichuan 13B with a B grade (Runs well). Expected decode speed: 17.9 tok/s.
How much VRAM does Baichuan 13B need?
Baichuan 13B (13B parameters) requires approximately 35.8 GB of memory with Q5_K_M quantization.
What is the best quantization for Baichuan 13B?
The recommended quantization for Baichuan 13B is Q5_K_M, which balances quality and memory efficiency.
What speed will Baichuan 13B run at on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, Baichuan 13B achieves approximately 17.9 tokens per second decode speed with a time-to-first-token of 10846ms using Q5_K_M quantization.
Can NVIDIA DGX Spark 128GB run Baichuan 13B for coding?
For coding workloads, Baichuan 13B on NVIDIA DGX Spark 128GB receives a B grade with 17.9 tok/s and 8K context.
What context window can Baichuan 13B use on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, Baichuan 13B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Baichuan 13B?
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.
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