Can solar finalised finetuned Model 10.7B i1 run on RTX 5080 16GB?

YES — Runs Great

B56Good

Estimated from fit model

solar finalised finetuned Model 10.7B i1 needs ~10.6 GB VRAM. RTX 5080 16GB has 16.0 GB. With Q4_K_M quantization, expect ~96 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 10.6 GB, 95.6 tok/s, Runs well

10.6 GB required16.0 GB available

66% VRAM used

Fit status

Runs well

Decode

95.6 tok/s

TTFT

2025 ms

Safe context

85K

Memory

10.6 GB / 16.0 GB

Memory breakdown

Weights6.5 GB

KV Cache1.3 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelssolar finalised finetuned Model 10.7B i1 on RTX 5080 16GB
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: 95.6 tok/s decode · 2.0s TTFT (warm) · 239 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	95.6 tok/s	1105 ms	85K
Coding	B	Runs well	95.6 tok/s	2025 ms	85K
Agentic Coding	B	Runs well	95.6 tok/s	2946 ms	85K
Reasoning	B	Runs well	95.6 tok/s	2393 ms	85K
RAG	B	Runs well	95.6 tok/s	3682 ms	85K

Inference speed

solar finalised finetuned Model 10.7B i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for solar finalised finetuned Model 10.7B i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~150 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	149.8	Fits
RTX 4090 24GB	24 GB	Q4_K_M	117.4	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	105.9	Fits
RTX 3090 24GB	24 GB	Q4_K_M	100.4	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	93.6	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	85.3	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	71.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	67.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	60.8	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	46.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	46.4	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	36.8	Fits
RTX 3060 12GB	12 GB	Q4_K_M	36.4	Tight
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	33.7	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	28.3	Fits
RTX 4060 8GB	8 GB	Q4_K_M	16.8	Heavy offload

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on RTX 5080 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	C48
Q3_K_S	3	5.2 GB	Low	C49
NVFP4	4	6.0 GB	Medium	C49
Q4_K_M	4	6.5 GB	Medium	C50
Q5_K_M	5	7.7 GB	High	C51
Q6_K	6	8.8 GB	High	C51
Q8_0Best for your GPU	8	11.4 GB	Very High	C50
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

Frequently asked questions

Can RTX 5080 16GB run solar finalised finetuned Model 10.7B i1?

Yes, RTX 5080 16GB can run solar finalised finetuned Model 10.7B i1 with a B grade (Runs well). Expected decode speed: 95.6 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 10.6 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 RTX 5080 16GB?

On RTX 5080 16GB, solar finalised finetuned Model 10.7B i1 achieves approximately 95.6 tokens per second decode speed with a time-to-first-token of 2025ms using Q4_K_M quantization.

Can RTX 5080 16GB run solar finalised finetuned Model 10.7B i1 for coding?

For coding workloads, solar finalised finetuned Model 10.7B i1 on RTX 5080 16GB receives a B grade with 95.6 tok/s and 85K context.

What context window can solar finalised finetuned Model 10.7B i1 use on RTX 5080 16GB?

On RTX 5080 16GB, solar finalised finetuned Model 10.7B i1 can safely use up to 85K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5080 16GB See all hardware for solar finalised finetuned Model 10.7B i1

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