Will It Run AI

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
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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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well33.0 tok/s3202 ms48K
CodingCRuns well33.0 tok/s5871 ms48K
Agentic CodingCTight fit33.0 tok/s8540 ms48K
ReasoningCRuns well33.0 tok/s6939 ms48K
RAGCTight fit33.0 tok/s10675 ms48K

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).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowC49
Q3_K_S
3
6.9 GB
LowC50
NVFP4
4
7.8 GB
MediumC51
Q4_K_M
4
8.5 GB
MediumC51
Q5_K_M
5
10.1 GB
HighC51
Q6_KBest for your GPU
6
11.5 GB
HighC50
Q8_0
8
15.0 GB
Very HighF0
F16
16
28.7 GB
MaximumF0

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

Opciones de mejora

Hardware que ejecuta bien GGUF SOLARized GraniStral 14B 1902 YeAM HCT

RX 7900 XT 20GBOpción económica
20 GB VRAM (+4)800 GB/s (+288)
C
Sube la velocidad estimada de decodificación alrededor de un 70%.56.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 70%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$899 MSRP

NVIDIARTX 4090 24GBMejor relación calidad-precio
24 GB VRAM (+8)1008 GB/s (+496)
C
Sube la velocidad estimada de decodificación alrededor de un 185%.94.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 185%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$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 16GBSee all hardware for GGUF SOLARized GraniStral 14B 1902 YeAM HCT
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