Will It Run AI

Can stablelm 2 1 6b chat imatrix run on Tesla P40 24GB?

YES — Runs Great

C47Usable
Estimated from fit model

stablelm 2 1 6b chat imatrix needs ~8.0 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~56 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.0 GB, 55.8 tok/s, Runs well
8.0 GB required24.0 GB available
33% VRAM used

Fit status

Runs well

Decode

55.8 tok/s

TTFT

3471 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 Tesla P40 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: 55.8 tok/s decode · 3.5s TTFT (warm) · 139 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well55.8 tok/s1893 ms381K
CodingCRuns well55.8 tok/s3471 ms381K
Agentic CodingCRuns well55.8 tok/s5049 ms381K
ReasoningCRuns well55.8 tok/s4102 ms381K
RAGCRuns well55.8 tok/s6311 ms381K

Quantization options

How stablelm 2 1 6b chat imatrix (6B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.3 GB
LowC44
Q3_K_S
3
2.9 GB
LowC44
NVFP4
4
3.4 GB
MediumC44
Q4_K_M
4
3.7 GB
MediumC45
Q5_K_M
5
4.3 GB
HighC45
Q6_K
6
4.9 GB
HighC45
Q8_0
8
6.4 GB
Very HighC46
F16Best for your GPU
16
12.3 GB
MaximumC50

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

Opções de upgrade

Hardware que roda bem stablelm 2 1 6b chat imatrix

NVIDIARTX 5090 32GBOpção econômica
32 GB VRAM (+8)1792 GB/s (+1446)
C
Raises estimated decode speed by about 51%.84 tok/s decodificação

Raises estimated decode speed by about 51%.

Adds memory headroom for longer context windows and future model growth.

~$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBMelhor custo-benefício
32 GB VRAM (+8)896 GB/s (+550)
C
Raises estimated decode speed by about 51%.84 tok/s decodificação

Raises estimated decode speed by about 51%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

NVIDIARTX 5000 Ada 32GBUpgrade NVIDIA
32 GB VRAM (+8)576 GB/s (+230)
C
Raises estimated decode speed by about 51%.84 tok/s decodificação

Raises estimated decode speed by about 51%.

Adds memory headroom for longer context windows and future model growth.

~$4,000 MSRP

Frequently asked questions

Can Tesla P40 24GB run stablelm 2 1 6b chat imatrix?

Yes, Tesla P40 24GB can run stablelm 2 1 6b chat imatrix with a C grade (Runs well). Expected decode speed: 55.8 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 Tesla P40 24GB?

On Tesla P40 24GB, stablelm 2 1 6b chat imatrix achieves approximately 55.8 tokens per second decode speed with a time-to-first-token of 3471ms using Q4_K_M quantization.

Can Tesla P40 24GB run stablelm 2 1 6b chat imatrix for coding?

For coding workloads, stablelm 2 1 6b chat imatrix on Tesla P40 24GB receives a C grade with 55.8 tok/s and 381K context.

What context window can stablelm 2 1 6b chat imatrix use on Tesla P40 24GB?

On Tesla P40 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.

See all results for Tesla P40 24GBSee all hardware for stablelm 2 1 6b chat imatrix
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