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URL: https://willitrunai.com/can-run/hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf-on-rx-6600-8gb

⇱ solar finalised finetuned Model 10.7B i1 on RX 6600 8GB? YES

Can solar finalised finetuned Model 10.7B i1 run on RX 6600 8GB?

BARELY — Tight on Memory

D30Poor
○Estimated from fit model

solar finalised finetuned Model 10.7B i1 needs ~9.5 GB VRAM. RX 6600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload
Share:

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) — 9.5 GB, 8.8 tok/s, Very compromised (needs ~1 GB host RAM)
9.5 GB required8.0 GB available
119% VRAM needed

1.5 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1 GB host RAM)

Decode

8.8 tok/s

TTFT

21947 ms

Safe context

4K

Memory

9.5 GB / 8.0 GB

Offload

20%

Memory breakdown

Weights6.5 GB
KV Cache1.3 GB
Runtime0.9 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelssolar finalised finetuned Model 10.7B i1 on RX 6600 8GB
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: 8.8 tok/s decode · 21.9s TTFT (warm) · 22 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatDVery compromised (needs ~0.6 GB host RAM)10.2 tok/s10366 ms4K
CodingDVery compromised (needs ~1 GB host RAM)8.8 tok/s21947 ms4K
Agentic CodingFToo heavy6.8 tok/s41463 ms4K
ReasoningDVery compromised (needs ~1 GB host RAM)8.8 tok/s25938 ms4K
RAGFToo heavy6.8 tok/s51829 ms4K

Quantization options

How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on RX 6600 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.2 GB
LowC53
Q3_K_SBest for your GPU
3
5.2 GB
LowC52
NVFP4
4
6.0 GB
MediumF0
Q4_K_M
4
6.5 GB
MediumF0
Q5_K_M
5
7.7 GB
HighF0
Q6_K
6
8.8 GB
HighF0
Q8_0
8
11.4 GB
Very HighF0
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

Upgrade options

Hardware that runs solar finalised finetuned Model 10.7B i1 well

RX 7600 XT 16GBBudget pick
16 GB VRAM (+8)288 GB/s (+64)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.25.6 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 191%.

~$329 MSRP

RX 9060 XT 16GBBest value
16 GB VRAM (+8)320 GB/s (+96)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.30.9 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 251%.

~$349 MSRP

RX 7700 XT 12GBAMD upgrade
12 GB VRAM (+4)432 GB/s (+208)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.39.7 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 351%.

~$449 MSRP

Frequently asked questions

See all results for RX 6600 8GBSee all hardware for solar finalised finetuned Model 10.7B i1