RX 7700 XT 12GB经济之选
12 GB VRAM (+4)432 GB/s (+152)
C
Raises estimated decode speed by about 82%.60.7 tok/s 解码
Raises estimated decode speed by about 82%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Can internlm2 5 7b chat i1 run on RX 6650 XT 8GB?
YES — Tight Fit
C50Usable
Estimated from fit model
internlm2 5 7b chat i1 needs ~6.8 GB VRAM. RX 6650 XT 8GB has 8.0 GB. With Q4_K_M quantization, expect ~33 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) — 6.8 GB, 33.4 tok/s, Tight fit
6.8 GB required8.0 GB available
85% VRAM used
Fit status
Tight fit
Decode
33.4 tok/s
TTFT
5789 ms
Safe context
40K
Memory
6.8 GB / 8.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsinternlm2 5 7b chat 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: 33.4 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 | 33.4 tok/s | 3158 ms | 40K |
| Coding | C | Tight fit | 33.4 tok/s | 5789 ms | 40K |
| Agentic Coding | C | Runs with offload | 33.4 tok/s | 8420 ms | 40K |
| Reasoning | C | Tight fit | 33.4 tok/s | 6842 ms | 40K |
| RAG | C | Runs with offload | 33.4 tok/s | 10525 ms | 40K |
Quantization options
How internlm2 5 7b chat i1 (7B params) fits at each quantization level on RX 6650 XT 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C53 |
Q3_K_S | 3 | 3.4 GB | Low | C53 |
NVFP4 | 4 | 3.9 GB | Medium | C53 |
Q4_K_M | 4 | 4.3 GB | Medium | C53 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | C52 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run internlm2 5 7b chat i1 on your machine.
Run
lms load hf-mradermacher--internlm2-5-7b-chat-i1-gguf && lms server start升级选项
能流畅运行 internlm2 5 7b chat i1 的硬件
RX 6700 XT 12GB最佳性价比
12 GB VRAM (+4)384 GB/s (+104)
C
Raises estimated decode speed by about 40%.46.8 tok/s 解码
Raises estimated decode speed by about 40%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
RX 9070 16GBAMD 升级
16 GB VRAM (+8)640 GB/s (+360)
C
Raises estimated decode speed by about 178%.92.9 tok/s 解码
Raises estimated decode speed by about 178%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
Frequently asked questions
Can RX 6650 XT 8GB run internlm2 5 7b chat i1?
Yes, RX 6650 XT 8GB can run internlm2 5 7b chat i1 with a C grade (Tight fit). Expected decode speed: 33.4 tok/s.
How much VRAM does internlm2 5 7b chat i1 need?
internlm2 5 7b chat i1 (7B parameters) requires approximately 6.8 GB of memory with Q4_K_M quantization.
What is the best quantization for internlm2 5 7b chat i1?
The recommended quantization for internlm2 5 7b chat i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will internlm2 5 7b chat i1 run at on RX 6650 XT 8GB?
On RX 6650 XT 8GB, internlm2 5 7b chat i1 achieves approximately 33.4 tokens per second decode speed with a time-to-first-token of 5789ms using Q4_K_M quantization.
Can RX 6650 XT 8GB run internlm2 5 7b chat i1 for coding?
For coding workloads, internlm2 5 7b chat i1 on RX 6650 XT 8GB receives a C grade with 33.4 tok/s and 40K context.
What context window can internlm2 5 7b chat i1 use on RX 6650 XT 8GB?
On RX 6650 XT 8GB, internlm2 5 7b chat i1 can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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