Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on RX 6800 16GB?

YES — Runs Great

C53Usable

Estimated from fit model

GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~12.7 GB VRAM. RX 6800 16GB has 16.0 GB. With Q4_K_M quantization, expect ~33 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) — 12.7 GB, 33.0 tok/s, Runs well

12.7 GB required16.0 GB available

79% VRAM used

Fit status

Runs well

Decode

33.0 tok/s

TTFT

5871 ms

Safe context

48K

Memory

12.7 GB / 16.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGGUF SOLARized GraniStral 14B 1902 YeAM HCT on RX 6800 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: 33.0 tok/s decode · 5.9s TTFT (warm) · 82 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	33.0 tok/s	3202 ms	48K
Coding	C	Runs well	33.0 tok/s	5871 ms	48K
Agentic Coding	C	Tight fit	33.0 tok/s	8540 ms	48K
Reasoning	C	Runs well	33.0 tok/s	6939 ms	48K
RAG	C	Tight fit	33.0 tok/s	10675 ms	48K

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on RX 6800 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C49
Q3_K_S	3	6.9 GB	Low	C50
NVFP4	4

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

Upgrade options

Hardware that runs GGUF SOLARized GraniStral 14B 1902 YeAM HCT well

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+288)

Raises estimated decode speed by about 70%.56.2 tok/s decode

Raises estimated decode speed by about 70%.

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

~$899 MSRP

RTX 4090 24GBBest value

24 GB VRAM (+8)1008 GB/s (+496)

Raises estimated decode speed by about 185%.94.2 tok/s decode

Raises estimated decode speed by about 185%.

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

~$1,599 MSRP

Frequently asked questions

Can RX 6800 16GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Yes, RX 6800 16GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a C grade (Runs well). Expected decode speed: 33.0 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 12.7 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 RX 6800 16GB?

On RX 6800 16GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 33.0 tokens per second decode speed with a time-to-first-token of 5871ms using Q4_K_M quantization.

Can RX 6800 16GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on RX 6800 16GB receives a C grade with 33.0 tok/s and 48K context.

What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on RX 6800 16GB?

On RX 6800 16GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT can safely use up to 48K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 6800 16GB See all hardware for GGUF SOLARized GraniStral 14B 1902 YeAM HCT

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