Will It Run AI

Can internlm3 8b instruct abliterated i1 run on RX 6800 16GB?

YES — Runs Great

C51Usable
Estimated from fit model

internlm3 8b instruct abliterated i1 needs ~8.3 GB VRAM. RX 6800 16GB has 16.0 GB. With Q4_K_M quantization, expect ~58 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) — 8.3 GB, 57.7 tok/s, Runs well
8.3 GB required16.0 GB available
52% VRAM used

Fit status

Runs well

Decode

57.7 tok/s

TTFT

3355 ms

Safe context

147K

Memory

8.3 GB / 16.0 GB

Memory breakdown

Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsinternlm3 8b instruct abliterated i1 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: 57.7 tok/s decode · 3.4s TTFT (warm) · 144 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 well57.7 tok/s1830 ms147K
CodingCRuns well57.7 tok/s3355 ms147K
Agentic CodingCRuns well57.7 tok/s4880 ms147K
ReasoningCRuns well57.7 tok/s3965 ms147K
RAGCRuns well57.7 tok/s6100 ms147K

Quantization options

How internlm3 8b instruct abliterated i1 (8B params) fits at each quantization level on RX 6800 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC47
Q3_K_S
3
3.9 GB
LowC47
NVFP4
4
4.5 GB
MediumC48
Q4_K_M
4
4.9 GB
MediumC48
Q5_K_M
5
5.8 GB
HighC49
Q6_K
6
6.6 GB
HighC50
Q8_0Best for your GPU
8
8.6 GB
Very HighC51
F16
16
16.4 GB
MaximumF0

Get started

Copy-paste commands to run internlm3 8b instruct abliterated i1 on your machine.

Run

lms load hf-mradermacher--internlm3-8b-instruct-abliterated-i1-gguf && lms server start

Frequently asked questions

Can RX 6800 16GB run internlm3 8b instruct abliterated i1?

Yes, RX 6800 16GB can run internlm3 8b instruct abliterated i1 with a C grade (Runs well). Expected decode speed: 57.7 tok/s.

How much VRAM does internlm3 8b instruct abliterated i1 need?

internlm3 8b instruct abliterated i1 (8B parameters) requires approximately 8.3 GB of memory with Q4_K_M quantization.

What is the best quantization for internlm3 8b instruct abliterated i1?

The recommended quantization for internlm3 8b instruct abliterated i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will internlm3 8b instruct abliterated i1 run at on RX 6800 16GB?

On RX 6800 16GB, internlm3 8b instruct abliterated i1 achieves approximately 57.7 tokens per second decode speed with a time-to-first-token of 3355ms using Q4_K_M quantization.

Can RX 6800 16GB run internlm3 8b instruct abliterated i1 for coding?

For coding workloads, internlm3 8b instruct abliterated i1 on RX 6800 16GB receives a C grade with 57.7 tok/s and 147K context.

What context window can internlm3 8b instruct abliterated i1 use on RX 6800 16GB?

On RX 6800 16GB, internlm3 8b instruct abliterated i1 can safely use up to 147K 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 internlm3 8b instruct abliterated i1
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