Can solar finalised finetuned Model 10.7B i1 run on AMD Instinct MI210 64GB?

YES — Runs Great

C47Usable

○Estimated from fit model

solar finalised finetuned Model 10.7B i1 needs ~15.1 GB VRAM. AMD Instinct MI210 64GB has 64.0 GB. With Q4_K_M quantization, expect ~150 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 15.1 GB, 149.8 tok/s, Runs well

15.1 GB required64.0 GB available

24% VRAM used

Fit status

Runs well

Decode

149.8 tok/s

TTFT

1292 ms

Safe context

640K

Memory

15.1 GB / 64.0 GB

Memory breakdown

Weights6.5 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelssolar finalised finetuned Model 10.7B i1 on AMD Instinct MI210 64GB
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: 149.8 tok/s decode · 1.3s TTFT (warm) · 375 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	149.8 tok/s	705 ms	640K
Coding	C	Runs well	149.8 tok/s	1292 ms	640K
Agentic Coding	C	Runs well	149.8 tok/s	1880 ms	640K
Reasoning	C	Runs well	149.8 tok/s	1527 ms	640K
RAG	C	Runs well	149.8 tok/s	2350 ms	640K

Quantization options

How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on AMD Instinct MI210 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	C40
Q3_K_S	3	5.2 GB	Low	C40
NVFP4	4	6.0 GB	Medium	C40
Q4_K_M	4	6.5 GB	Medium	C40
Q5_K_M	5	7.7 GB	High	C40
Q6_K	6	8.8 GB	High	C41
Q8_0	8	11.4 GB	Very High	C41
F16Best for your GPU	16	21.9 GB	Maximum	C43