RX 7900 XT 20GBBudget pick
20 GB VRAM (+4)800 GB/s (+240)
C
Raises estimated decode speed by about 91%.98.4 tok/s decode
Raises estimated decode speed by about 91%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
internlm2 5 1 8b chat i1 needs ~8.3 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~52 tok/s.
Operating mode
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
Fit status
Runs well
Decode
51.6 tok/s
TTFT
3749 ms
Safe context
147K
Memory
8.3 GB / 16.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 51.6 tok/s | 2045 ms | 147K |
| Coding | C | Runs well | 51.6 tok/s | 3749 ms | 147K |
| Agentic Coding | C | Runs well | 51.6 tok/s | 5453 ms | 147K |
| Reasoning | C | Runs well | 51.6 tok/s | 4431 ms | 147K |
| RAG | C | Runs well | 51.6 tok/s | 6817 ms | 147K |
How internlm2 5 1 8b chat i1 (8B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C47 |
Q3_K_S | 3 | 3.9 GB | Low | C47 |
NVFP4 | 4 |
Copy-paste commands to run internlm2 5 1 8b chat i1 on your machine.
Run
lms load hf-mradermacher--internlm2-5-1-8b-chat-i1-gguf && lms server startUpgrade options
Raises estimated decode speed by about 91%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
RTX A4500 20GBBest valueRaises estimated decode speed by about 98%.
Adds memory headroom for longer context windows and future model growth.
This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.
~$2,000 MSRP
Yes, Intel Arc A770 16GB can run internlm2 5 1 8b chat i1 with a C grade (Runs well). Expected decode speed: 51.6 tok/s.
internlm2 5 1 8b chat i1 (8B parameters) requires approximately 8.3 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm2 5 1 8b chat i1 is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A770 16GB, internlm2 5 1 8b chat i1 achieves approximately 51.6 tokens per second decode speed with a time-to-first-token of 3749ms using Q4_K_M quantization.
For coding workloads, internlm2 5 1 8b chat i1 on Intel Arc A770 16GB receives a C grade with 51.6 tok/s and 147K context.
On Intel Arc A770 16GB, internlm2 5 1 8b chat i1 can safely use up to 147K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--internlm2-5-1-8b-chat-i1-gguf-on-arc-a770-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
4.5 GB |
| Medium |
| C48 |
Q4_K_M | 4 | 4.9 GB | Medium | C48 |
Q5_K_M | 5 | 5.8 GB | High | C49 |
Q6_K | 6 | 6.6 GB | High | C50 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | C51 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.