Can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run on Radeon Pro W6800 32GB?

YES — Runs Great

C48Usable

Estimated from fit model

GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV needs ~14.3 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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, 33.6 tok/s, Runs well

14.3 GB required32.0 GB available

45% VRAM used

Fit status

Runs well

Decode

33.6 tok/s

TTFT

5766 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 2102 YeAM HCT 32QKV on Radeon Pro W6800 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: 33.6 tok/s decode · 5.8s TTFT (warm) · 84 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	33.6 tok/s	3145 ms	189K
Coding	C	Runs well	33.6 tok/s	5766 ms	189K
Agentic Coding	C	Runs well	33.6 tok/s	8387 ms	189K
Reasoning	C	Runs well	33.6 tok/s	6815 ms	189K
RAG	C	Runs well	33.6 tok/s	10484 ms	189K

Quantization options

How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C44
Q3_K_S	3	6.9 GB	Low	C44
NVFP4	4

Get started

Copy-paste commands to run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on your machine.

Run

lms load hf-srs6901--gguf-solarized-granistral-14b-2102-yeam-hct-32qkv && lms server start

Upgrade options

Hardware that runs GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV well

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)546 GB/s (+34)

This setup is broadly balanced for this model.35.4 tok/s decode

~$2,499 MSRP

Mac Studio M2 Ultra 64GBBest value

64 GB Unified (+32)800 GB/s (+288)

Raises estimated decode speed by about 62%.54.3 tok/s decode

Raises estimated decode speed by about 62%.

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

~$3,999 MSRP

Frequently asked questions

Can Radeon Pro W6800 32GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

Yes, Radeon Pro W6800 32GB can run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV with a C grade (Runs well). Expected decode speed: 33.6 tok/s.

How much VRAM does GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV need?

GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B parameters) requires approximately 14.3 GB of memory with Q4_K_M quantization.

What is the best quantization for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

The recommended quantization for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV is Q4_K_M, which balances quality and memory efficiency.

What speed will GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run at on Radeon Pro W6800 32GB?

On Radeon Pro W6800 32GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV achieves approximately 33.6 tokens per second decode speed with a time-to-first-token of 5766ms using Q4_K_M quantization.

Can Radeon Pro W6800 32GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on Radeon Pro W6800 32GB receives a C grade with 33.6 tok/s and 189K context.

What context window can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV use on Radeon Pro W6800 32GB?

See all results for Radeon Pro W6800 32GB See all hardware for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV

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