Will It Run AI

Can solar finalised finetuned Model 10.7B i1 run on Tesla P40 24GB?

YES — Runs Great

C48Usable
Estimated from fit model

solar finalised finetuned Model 10.7B i1 needs ~11.4 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~31 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) — 11.4 GB, 31.3 tok/s, Runs well
11.4 GB required24.0 GB available
48% VRAM used

Fit status

Runs well

Decode

31.3 tok/s

TTFT

6190 ms

Safe context

177K

Memory

11.4 GB / 24.0 GB

Memory breakdown

Weights6.5 GB
KV Cache1.3 GB
Runtime1.2 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelssolar finalised finetuned Model 10.7B i1 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: 31.3 tok/s decode · 6.2s TTFT (warm) · 78 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 well31.3 tok/s3376 ms177K
CodingCRuns well31.3 tok/s6190 ms177K
Agentic CodingCRuns well31.3 tok/s9004 ms177K
ReasoningCRuns well31.3 tok/s7315 ms177K
RAGCRuns well31.3 tok/s11255 ms177K

Quantization options

How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.2 GB
LowC45
Q3_K_S
3
5.2 GB
LowC45
NVFP4
4
6.0 GB
MediumC46
Q4_K_M
4
6.5 GB
MediumC46
Q5_K_M
5
7.7 GB
HighC47
Q6_K
6
8.8 GB
HighC47
Q8_0Best for your GPU
8
11.4 GB
Very HighC49
F16
16
21.9 GB
MaximumF0

Get started

Copy-paste commands to run solar finalised finetuned Model 10.7B i1 on your machine.

Run

lms load hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf && lms server start

アップグレードオプション

solar finalised finetuned Model 10.7B i1を快適に動かすハードウェア

NVIDIARTX 5090 32GBコスパ重視
32 GB VRAM (+8)1792 GB/s (+1446)
C
Raises estimated decode speed by about 379%.149.8 tok/sデコード

Raises estimated decode speed by about 379%.

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

〜$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBコスパ最良
32 GB VRAM (+8)896 GB/s (+550)
C
Raises estimated decode speed by about 268%.115.3 tok/sデコード

Raises estimated decode speed by about 268%.

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

〜$2,499 MSRP

NVIDIARTX 5000 Ada 32GBNVIDIAアップグレード
32 GB VRAM (+8)576 GB/s (+230)
C
Raises estimated decode speed by about 126%.70.6 tok/sデコード

Raises estimated decode speed by about 126%.

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

〜$4,000 MSRP

Frequently asked questions

Can Tesla P40 24GB run solar finalised finetuned Model 10.7B i1?

Yes, Tesla P40 24GB can run solar finalised finetuned Model 10.7B i1 with a C grade (Runs well). Expected decode speed: 31.3 tok/s.

How much VRAM does solar finalised finetuned Model 10.7B i1 need?

solar finalised finetuned Model 10.7B i1 (10.699999809265137B parameters) requires approximately 11.4 GB of memory with Q4_K_M quantization.

What is the best quantization for solar finalised finetuned Model 10.7B i1?

The recommended quantization for solar finalised finetuned Model 10.7B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will solar finalised finetuned Model 10.7B i1 run at on Tesla P40 24GB?

On Tesla P40 24GB, solar finalised finetuned Model 10.7B i1 achieves approximately 31.3 tokens per second decode speed with a time-to-first-token of 6190ms using Q4_K_M quantization.

Can Tesla P40 24GB run solar finalised finetuned Model 10.7B i1 for coding?

For coding workloads, solar finalised finetuned Model 10.7B i1 on Tesla P40 24GB receives a C grade with 31.3 tok/s and 177K context.

What context window can solar finalised finetuned Model 10.7B i1 use on Tesla P40 24GB?

On Tesla P40 24GB, solar finalised finetuned Model 10.7B i1 can safely use up to 177K 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 solar finalised finetuned Model 10.7B i1
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