Can solar finalised finetuned Model 10.7B i1 run on NVIDIA L4 24GB?

YES — Runs Great

C48Usable

Estimated from fit model

solar finalised finetuned Model 10.7B i1 needs ~11.4 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~30 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 11.4 GB, 29.9 tok/s, Runs well

11.4 GB required24.0 GB available

48% VRAM used

Fit status

Runs well

Decode

29.9 tok/s

TTFT

6480 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 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: 29.9 tok/s decode · 6.5s TTFT (warm) · 75 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	29.9 tok/s	3535 ms	177K
Coding	C	Runs well	29.9 tok/s	6480 ms	177K
Agentic Coding	C	Runs well	29.9 tok/s	9426 ms	177K
Reasoning	C	Runs well	29.9 tok/s	7658 ms	177K
RAG	C	Runs well	29.9 tok/s	11782 ms	177K

Quantization options

How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	C45
Q3_K_S	3	5.2 GB	Low	C45
NVFP4	4	6.0 GB	Medium	C46
Q4_K_M	4	6.5 GB	Medium	C46
Q5_K_M	5	7.7 GB	High	C47
Q6_K	6	8.8 GB	High	C47
Q8_0Best for your GPU	8	11.4 GB	Very High	C49
F16	16	21.9 GB	Maximum	F0

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

Opções de upgrade

Hardware que roda bem solar finalised finetuned Model 10.7B i1

RTX 5090 32GBOpção econômica

32 GB VRAM (+8)1792 GB/s (+1492)

Raises estimated decode speed by about 401%.149.8 tok/s decodificação

Raises estimated decode speed by about 401%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMelhor custo-benefício

32 GB VRAM (+8)896 GB/s (+596)

Raises estimated decode speed by about 286%.115.3 tok/s decodificação

Raises estimated decode speed by about 286%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBUpgrade NVIDIA

32 GB VRAM (+8)576 GB/s (+276)

Raises estimated decode speed by about 136%.70.6 tok/s decodificação

Raises estimated decode speed by about 136%.

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

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run solar finalised finetuned Model 10.7B i1?

Yes, NVIDIA L4 24GB can run solar finalised finetuned Model 10.7B i1 with a C grade (Runs well). Expected decode speed: 29.9 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, solar finalised finetuned Model 10.7B i1 achieves approximately 29.9 tokens per second decode speed with a time-to-first-token of 6480ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run solar finalised finetuned Model 10.7B i1 for coding?

For coding workloads, solar finalised finetuned Model 10.7B i1 on NVIDIA L4 24GB receives a C grade with 29.9 tok/s and 177K context.

What context window can solar finalised finetuned Model 10.7B i1 use on NVIDIA L4 24GB?

On NVIDIA L4 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 NVIDIA L4 24GB See all hardware for solar finalised finetuned Model 10.7B i1

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