Will It Run AI

Can SOLAR 10.7B v1.0 run on RX 9060 XT 16GB?

YES — Runs Great

C52Usable
Estimated from fit model

SOLAR 10.7B v1.0 needs ~10.3 GB VRAM. RX 9060 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~31 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
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) — 10.3 GB, 30.9 tok/s, Runs well
10.3 GB required16.0 GB available
64% VRAM used

Fit status

Runs well

Decode

30.9 tok/s

TTFT

6268 ms

Safe context

89K

Memory

10.3 GB / 16.0 GB

Memory breakdown

Weights6.5 GB
KV Cache1.3 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsSOLAR 10.7B v1.0 on RX 9060 XT 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: 30.9 tok/s decode · 6.3s TTFT (warm) · 77 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well30.9 tok/s3419 ms89K
CodingCRuns well30.9 tok/s6268 ms89K
Agentic CodingCRuns well30.9 tok/s9117 ms89K
ReasoningCRuns well30.9 tok/s7408 ms89K
RAGCRuns well30.9 tok/s11396 ms89K

Quantization options

How SOLAR 10.7B v1.0 (10.699999809265137B params) fits at each quantization level on RX 9060 XT 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.2 GB
LowC48
Q3_K_S
3
5.2 GB
LowC49
NVFP4
4
6.0 GB
MediumC49
Q4_K_M
4
6.5 GB
MediumC50
Q5_K_M
5
7.7 GB
HighC51
Q6_K
6
8.8 GB
HighC51
Q8_0Best for your GPU
8
11.4 GB
Very HighC50
F16
16
21.9 GB
MaximumF0

Get started

Copy-paste commands to run SOLAR 10.7B v1.0 on your machine.

Run

lms load hf-mradermacher--solar-10-7b-v1-0-gguf && lms server start

Upgrade-Optionen

Hardware, die SOLAR 10.7B v1.0 gut ausführt

RX 7900 XT 20GBGünstige Wahl
20 GB VRAM (+4)800 GB/s (+480)
C
Raises estimated decode speed by about 138%.73.5 tok/s Dekodierung

Raises estimated decode speed by about 138%.

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

ca. $899 MSRP

NVIDIARTX A4500 20GBBestes Preis-Leistungs-Verhältnis
20 GB VRAM (+4)640 GB/s (+320)
C
Raises estimated decode speed by about 148%.76.5 tok/s Dekodierung

Raises estimated decode speed by about 148%.

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

ca. $2,000 MSRP

Frequently asked questions

Can RX 9060 XT 16GB run SOLAR 10.7B v1.0?

Yes, RX 9060 XT 16GB can run SOLAR 10.7B v1.0 with a C grade (Runs well). Expected decode speed: 30.9 tok/s.

How much VRAM does SOLAR 10.7B v1.0 need?

SOLAR 10.7B v1.0 (10.699999809265137B parameters) requires approximately 10.3 GB of memory with Q4_K_M quantization.

What is the best quantization for SOLAR 10.7B v1.0?

The recommended quantization for SOLAR 10.7B v1.0 is Q4_K_M, which balances quality and memory efficiency.

What speed will SOLAR 10.7B v1.0 run at on RX 9060 XT 16GB?

On RX 9060 XT 16GB, SOLAR 10.7B v1.0 achieves approximately 30.9 tokens per second decode speed with a time-to-first-token of 6268ms using Q4_K_M quantization.

Can RX 9060 XT 16GB run SOLAR 10.7B v1.0 for coding?

For coding workloads, SOLAR 10.7B v1.0 on RX 9060 XT 16GB receives a C grade with 30.9 tok/s and 89K context.

What context window can SOLAR 10.7B v1.0 use on RX 9060 XT 16GB?

On RX 9060 XT 16GB, SOLAR 10.7B v1.0 can safely use up to 89K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 9060 XT 16GBSee all hardware for SOLAR 10.7B v1.0
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-mradermacher--solar-10-7b-v1-0-gguf-on-rx-9060-xt-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: