Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on AMD Instinct MI60 32GB?

YES — Runs Great

C50Usable

Estimated from fit model

GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~14.3 GB VRAM. AMD Instinct MI60 32GB has 32.0 GB. With Q4_K_M quantization, expect ~59 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) — 14.3 GB, 58.8 tok/s, Runs well

14.3 GB required32.0 GB available

45% VRAM used

Fit status

Runs well

Decode

58.8 tok/s

TTFT

3295 ms

Safe context

189K

Memory

14.3 GB / 32.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGGUF SOLARized GraniStral 14B 1902 YeAM HCT on AMD Instinct MI60 32GB
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: 58.8 tok/s decode · 3.3s TTFT (warm) · 147 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	58.8 tok/s	1797 ms	189K
Coding	C	Runs well	58.8 tok/s	3295 ms	189K
Agentic Coding	C	Runs well	58.8 tok/s	4793 ms	189K
Reasoning	C	Runs well	58.8 tok/s	3894 ms	189K
RAG	C	Runs well	58.8 tok/s	5991 ms	189K

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on AMD Instinct MI60 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C43
Q3_K_S	3	6.9 GB	Low	C44
NVFP4	4	7.8 GB	Medium	C44
Q4_K_M	4	8.5 GB	Medium	C45
Q5_K_M	5	10.1 GB	High	C45
Q6_K	6	11.5 GB	High	C46
Q8_0Best for your GPU	8	15.0 GB	Very High	C48
F16	16	28.7 GB	Maximum	F0

Get started

Copy-paste commands to run GGUF SOLARized GraniStral 14B 1902 YeAM HCT on your machine.

Run

lms load hf-srs6901--gguf-solarized-granistral-14b-1902-yeam-hct && lms server start

Frequently asked questions

Can AMD Instinct MI60 32GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Yes, AMD Instinct MI60 32GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a C grade (Runs well). Expected decode speed: 58.8 tok/s.

How much VRAM does GGUF SOLARized GraniStral 14B 1902 YeAM HCT need?

GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B parameters) requires approximately 14.3 GB of memory with Q4_K_M quantization.

What is the best quantization for GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

The recommended quantization for GGUF SOLARized GraniStral 14B 1902 YeAM HCT is Q4_K_M, which balances quality and memory efficiency.

What speed will GGUF SOLARized GraniStral 14B 1902 YeAM HCT run at on AMD Instinct MI60 32GB?

On AMD Instinct MI60 32GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 58.8 tokens per second decode speed with a time-to-first-token of 3295ms using Q4_K_M quantization.

Can AMD Instinct MI60 32GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on AMD Instinct MI60 32GB receives a C grade with 58.8 tok/s and 189K context.

What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on AMD Instinct MI60 32GB?

On AMD Instinct MI60 32GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT can safely use up to 189K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for AMD Instinct MI60 32GB See all hardware for GGUF SOLARized GraniStral 14B 1902 YeAM HCT

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

58.8 tok/s

1797 ms

189K

Coding

Runs well

58.8 tok/s

3295 ms

189K

Agentic Coding

Runs well

58.8 tok/s

4793 ms

189K

Reasoning

Runs well

58.8 tok/s

3894 ms

189K

RAG

Runs well

58.8 tok/s

5991 ms

189K

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on AMD Instinct MI60 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C43
Q3_K_S	3	6.9 GB	Low	C44
NVFP4	4	7.8 GB	Medium	C44
Q4_K_M	4	8.5 GB	Medium	C45
Q5_K_M	5	10.1 GB	High	C45
Q6_K	6	11.5 GB	High	C46
Q8_0Best for your GPU	8	15.0 GB	Very High	C48
F16	16	28.7 GB	Maximum	F0