stablelm 2 1 6b chat imatrix needs ~18.6 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~45 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
44.8 tok/s
TTFT
4326 ms
Safe context
2.1M
Memory
18.6 GB / 108.8 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 44.8 tok/s | 2360 ms | 2.1M |
| Coding | C | Runs well | 44.8 tok/s | 4326 ms | 2.1M |
| Agentic Coding | C | Runs well | 44.8 tok/s | 6292 ms | 2.1M |
| Reasoning | C | Runs well | 44.8 tok/s | 5112 ms | 2.1M |
| RAG | C | Runs well | 44.8 tok/s | 7865 ms | 2.1M |
How stablelm 2 1 6b chat imatrix (6B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | D39 |
Q3_K_S | 3 | 2.9 GB | Low | D39 |
NVFP4 | 4 | 3.4 GB | Medium | D39 |
Q4_K_M | 4 | 3.7 GB | Medium | D39 |
Q5_K_M | 5 | 4.3 GB | High | D39 |
Q6_K | 6 | 4.9 GB | High | D39 |
Q8_0 | 8 | 6.4 GB | Very High | D39 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | D40 |
Copy-paste commands to run stablelm 2 1 6b chat imatrix on your machine.
Run
lms load hf-crataco--stablelm-2-1-6b-chat-imatrix-gguf && lms server startOpções de upgrade
NVIDIA H200 141GBOpção econômicaRaises estimated decode speed by about 88%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBMelhor custo-benefícioRaises estimated decode speed by about 88%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA B200 180GBUpgrade NVIDIARaises estimated decode speed by about 88%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run stablelm 2 1 6b chat imatrix with a C grade (Runs well). Expected decode speed: 44.8 tok/s.
stablelm 2 1 6b chat imatrix (6B parameters) requires approximately 18.6 GB of memory with Q4_K_M quantization.
The recommended quantization for stablelm 2 1 6b chat imatrix is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA DGX Spark 128GB, stablelm 2 1 6b chat imatrix achieves approximately 44.8 tokens per second decode speed with a time-to-first-token of 4326ms using Q4_K_M quantization.
For coding workloads, stablelm 2 1 6b chat imatrix on NVIDIA DGX Spark 128GB receives a C grade with 44.8 tok/s and 2.1M context.
On NVIDIA DGX Spark 128GB, stablelm 2 1 6b chat imatrix can safely use up to 2.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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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