MacBook Pro M4 Max 48GBBudget pick
48 GB Unified (+16)
C
This setup is broadly balanced for this model.84 tok/s decode
~$2,499 MSRP
stablelm 2 1 6b chat imatrix needs ~8.5 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~114 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
114.0 tok/s
TTFT
1698 ms
Safe context
552K
Memory
8.5 GB / 32.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 114.0 tok/s | 926 ms | 552K |
| Coding | C | Runs well | 114.0 tok/s | 1698 ms | 552K |
| Agentic Coding | C | Runs well | 114.0 tok/s | 2470 ms | 552K |
| Reasoning | C | Runs well | 114.0 tok/s | 2007 ms | 552K |
| RAG | C | Runs well | 114.0 tok/s | 3088 ms | 552K |
How stablelm 2 1 6b chat imatrix (6B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C43 |
Q3_K_S | 3 | 2.9 GB | Low | C43 |
NVFP4 | 4 | 3.4 GB | Medium | C43 |
Q4_K_M | 4 | 3.7 GB | Medium | C43 |
Q5_K_M | 5 | 4.3 GB | High | C43 |
Q6_K | 6 | 4.9 GB | High | C43 |
Q8_0 | 8 | 6.4 GB | Very High | C44 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C47 |
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 startUpgrade options
Yes, RTX 5090 32GB can run stablelm 2 1 6b chat imatrix with a C grade (Runs well). Expected decode speed: 114.0 tok/s.
stablelm 2 1 6b chat imatrix (6B parameters) requires approximately 8.5 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 RTX 5090 32GB, stablelm 2 1 6b chat imatrix achieves approximately 114.0 tokens per second decode speed with a time-to-first-token of 1698ms using Q4_K_M quantization.
For coding workloads, stablelm 2 1 6b chat imatrix on RTX 5090 32GB receives a C grade with 114.0 tok/s and 552K context.
On RTX 5090 32GB, stablelm 2 1 6b chat imatrix can safely use up to 552K 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-crataco--stablelm-2-1-6b-chat-imatrix-gguf-on-rtx-5090-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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