RX 9060 XT 16GBコスパ重視
16 GB VRAM (+8)320 GB/s (+40)
C
Raises estimated decode speed by about 41%.41.3 tok/sデコード
Raises estimated decode speed by about 41%.
Adds memory headroom for longer context windows and future model growth.
〜$349 MSRP
Can internlm3 8b instruct abliterated i1 run on RX 6650 XT 8GB?
YES — Tight Fit
C50Usable
Estimated from fit model
internlm3 8b instruct abliterated i1 needs ~7.5 GB VRAM. RX 6650 XT 8GB has 8.0 GB. With Q4_K_M quantization, expect ~29 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 7.5 GB, 29.3 tok/s, Tight fit
7.5 GB required8.0 GB available
94% VRAM used
Fit status
Tight fit
Decode
29.3 tok/s
TTFT
6616 ms
Safe context
24K
Memory
7.5 GB / 8.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsinternlm3 8b instruct abliterated i1 on RX 6650 XT 8GB
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: 29.3 tok/s decode · 6.6s TTFT (warm) · 73 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 29.3 tok/s | 3609 ms | 24K |
| Coding | C | Tight fit | 29.3 tok/s | 6616 ms | 24K |
| Agentic Coding | D | Runs with offload | 19.5 tok/s | 14416 ms | 24K |
| Reasoning | C | Tight fit | 29.3 tok/s | 7819 ms | 24K |
| RAG | D | Runs with offload | 19.5 tok/s | 18020 ms | 24K |
Quantization options
How internlm3 8b instruct abliterated i1 (8B params) fits at each quantization level on RX 6650 XT 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C53 |
Q3_K_S | 3 | 3.9 GB | Low | C53 |
NVFP4 | 4 | 4.5 GB | Medium | C53 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | C52 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
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アップグレードオプション
internlm3 8b instruct abliterated i1を快適に動かすハードウェア
RX 7700 XT 12GBコスパ最良
12 GB VRAM (+4)432 GB/s (+152)
C
Raises estimated decode speed by about 81%.53.1 tok/sデコード
Raises estimated decode speed by about 81%.
Adds memory headroom for longer context windows and future model growth.
〜$449 MSRP
RX 6700 XT 12GBAMDアップグレード
12 GB VRAM (+4)384 GB/s (+104)
C
Raises estimated decode speed by about 40%.40.9 tok/sデコード
Raises estimated decode speed by about 40%.
Adds memory headroom for longer context windows and future model growth.
〜$479 MSRP
Frequently asked questions
Can RX 6650 XT 8GB run internlm3 8b instruct abliterated i1?
Yes, RX 6650 XT 8GB can run internlm3 8b instruct abliterated i1 with a C grade (Tight fit). Expected decode speed: 29.3 tok/s.
How much VRAM does internlm3 8b instruct abliterated i1 need?
internlm3 8b instruct abliterated i1 (8B parameters) requires approximately 7.5 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 6650 XT 8GB?
On RX 6650 XT 8GB, internlm3 8b instruct abliterated i1 achieves approximately 29.3 tokens per second decode speed with a time-to-first-token of 6616ms using Q4_K_M quantization.
Can RX 6650 XT 8GB run internlm3 8b instruct abliterated i1 for coding?
For coding workloads, internlm3 8b instruct abliterated i1 on RX 6650 XT 8GB receives a C grade with 29.3 tok/s and 24K context.
What context window can internlm3 8b instruct abliterated i1 use on RX 6650 XT 8GB?
On RX 6650 XT 8GB, internlm3 8b instruct abliterated i1 can safely use up to 24K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if internlm3 8b instruct abliterated i1 feels slow on RX 6650 XT 8GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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