Can solar finalised finetuned Model 10.7B i1 run on RX 6750 XT 12GB?

YES — Tight Fit

C50Usable

Estimated from fit model

solar finalised finetuned Model 10.7B i1 needs ~9.9 GB VRAM. RX 6750 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: 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) — 9.9 GB, 35.1 tok/s, Tight fit

9.9 GB required12.0 GB available

83% VRAM used

Fit status

Tight fit

Decode

35.1 tok/s

TTFT

5519 ms

Safe context

43K

Memory

9.9 GB / 12.0 GB

Memory breakdown

Weights6.5 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelssolar finalised finetuned Model 10.7B i1 on RX 6750 XT 12GB
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: 35.1 tok/s decode · 5.5s TTFT (warm) · 88 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	35.1 tok/s	3010 ms	43K
Coding	C	Tight fit	35.1 tok/s	5519 ms	43K
Agentic Coding	C	Tight fit	35.1 tok/s	8028 ms	43K
Reasoning	C	Tight fit	35.1 tok/s	6522 ms	43K
RAG	C	Tight fit	35.1 tok/s	10035 ms	43K

Quantization options

How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on RX 6750 XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	C51
Q3_K_S	3	5.2 GB	Low	C52
NVFP4	4

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

Upgrade options

Hardware that runs solar finalised finetuned Model 10.7B i1 well

RX 7600 XT 16GBBudget pick

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.25.6 tok/s decode

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

~$329 MSRP

RX 9060 XT 16GBBest value

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.30.9 tok/s decode

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

~$349 MSRP

RX 9070 16GBAMD upgrade

16 GB VRAM (+4)640 GB/s (+208)

Raises estimated decode speed by about 73%.60.8 tok/s decode

Raises estimated decode speed by about 73%.

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

~$479 MSRP

Frequently asked questions

Can RX 6750 XT 12GB run solar finalised finetuned Model 10.7B i1?

Yes, RX 6750 XT 12GB can run solar finalised finetuned Model 10.7B i1 with a C grade (Tight fit). Expected decode speed: 35.1 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 9.9 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 RX 6750 XT 12GB?

On RX 6750 XT 12GB, solar finalised finetuned Model 10.7B i1 achieves approximately 35.1 tokens per second decode speed with a time-to-first-token of 5519ms using Q4_K_M quantization.

Can RX 6750 XT 12GB run solar finalised finetuned Model 10.7B i1 for coding?

For coding workloads, solar finalised finetuned Model 10.7B i1 on RX 6750 XT 12GB receives a C grade with 35.1 tok/s and 43K context.

What context window can solar finalised finetuned Model 10.7B i1 use on RX 6750 XT 12GB?

On RX 6750 XT 12GB, solar finalised finetuned Model 10.7B i1 can safely use up to 43K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 6750 XT 12GB See all hardware for solar finalised finetuned Model 10.7B i1

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