RTX 5090 32GBコスパ重視32 GB VRAM (+8)1792 GB/s (+1492)
C
Raises estimated decode speed by about 58%.84 tok/sデコード
Raises estimated decode speed by about 58%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Can stablelm 2 1 6b chat imatrix run on NVIDIA L4 24GB?
YES — Runs Great
C47Usable
Estimated from fit model
stablelm 2 1 6b chat imatrix needs ~8.0 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~53 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
Q4_K_M (Medium quality) — 8.0 GB, 53.3 tok/s, Runs well
8.0 GB required24.0 GB available
33% VRAM used
Fit status
Runs well
Decode
53.3 tok/s
TTFT
3634 ms
Safe context
381K
Memory
8.0 GB / 24.0 GB
Memory breakdown
Weights3.7 GB
KV Cache0.7 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsstablelm 2 1 6b chat imatrix on NVIDIA L4 24GB
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: 53.3 tok/s decode · 3.6s TTFT (warm) · 133 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 53.3 tok/s | 1982 ms | 381K |
| Coding | C | Runs well | 53.3 tok/s | 3634 ms | 381K |
| Agentic Coding | C | Runs well | 53.3 tok/s | 5285 ms | 381K |
| Reasoning | C | Runs well | 53.3 tok/s | 4294 ms | 381K |
| RAG | C | Runs well | 53.3 tok/s | 6607 ms | 381K |
Quantization options
How stablelm 2 1 6b chat imatrix (6B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C44 |
Q3_K_S | 3 | 2.9 GB | Low | C44 |
NVFP4 | 4 | 3.4 GB | Medium | C44 |
Q4_K_M | 4 | 3.7 GB | Medium | C45 |
Q5_K_M | 5 | 4.3 GB | High | C45 |
Q6_K | 6 | 4.9 GB | High | C45 |
Q8_0 | 8 | 6.4 GB | Very High | C46 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C50 |
Get started
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 startアップグレードオプション
stablelm 2 1 6b chat imatrixを快適に動かすハードウェア
RTX 5090 32GBコスパ重視RTX PRO 4500 Blackwell 32GBコスパ最良32 GB VRAM (+8)896 GB/s (+596)
C
Raises estimated decode speed by about 58%.84 tok/sデコード
Raises estimated decode speed by about 58%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
RTX 5000 Ada 32GBNVIDIAアップグレード32 GB VRAM (+8)576 GB/s (+276)
C
Raises estimated decode speed by about 58%.84 tok/sデコード
Raises estimated decode speed by about 58%.
Adds memory headroom for longer context windows and future model growth.
〜$4,000 MSRP
Frequently asked questions
Can NVIDIA L4 24GB run stablelm 2 1 6b chat imatrix?
Yes, NVIDIA L4 24GB can run stablelm 2 1 6b chat imatrix with a C grade (Runs well). Expected decode speed: 53.3 tok/s.
How much VRAM does stablelm 2 1 6b chat imatrix need?
stablelm 2 1 6b chat imatrix (6B parameters) requires approximately 8.0 GB of memory with Q4_K_M quantization.
What is the best quantization for stablelm 2 1 6b chat imatrix?
The recommended quantization for stablelm 2 1 6b chat imatrix is Q4_K_M, which balances quality and memory efficiency.
What speed will stablelm 2 1 6b chat imatrix run at on NVIDIA L4 24GB?
On NVIDIA L4 24GB, stablelm 2 1 6b chat imatrix achieves approximately 53.3 tokens per second decode speed with a time-to-first-token of 3634ms using Q4_K_M quantization.
Can NVIDIA L4 24GB run stablelm 2 1 6b chat imatrix for coding?
For coding workloads, stablelm 2 1 6b chat imatrix on NVIDIA L4 24GB receives a C grade with 53.3 tok/s and 381K context.
What context window can stablelm 2 1 6b chat imatrix use on NVIDIA L4 24GB?
On NVIDIA L4 24GB, stablelm 2 1 6b chat imatrix can safely use up to 381K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-crataco--stablelm-2-1-6b-chat-imatrix-gguf-on-l4-24gb" 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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