RTX 3060 12GBBudget pick12 GB VRAM (+4)360 GB/s (+104)
C
Raises estimated decode speed by about 27%.48.7 tok/s decode
Raises estimated decode speed by about 27%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Llama 3 8B Instruct 32k v0.1 needs ~7.8 GB VRAM. RTX 2000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~38 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 with offload
Decode
38.3 tok/s
TTFT
5055 ms
Safe context
19K
Memory
7.8 GB / 8.0 GB
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 38.3 tok/s | 2758 ms | 19K |
| Coding | C | Runs with offload | 38.3 tok/s | 5055 ms | 19K |
| Agentic Coding | D | Very compromised (needs ~0.4 GB host RAM) | 23.8 tok/s | 11854 ms | 19K |
| Reasoning | C | Runs with offload | 38.3 tok/s | 5975 ms | 19K |
| RAG | D | Very compromised (needs ~0.4 GB host RAM) | 23.8 tok/s | 14818 ms |
How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on RTX 2000 Ada Laptop 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C54 |
Q3_K_S | 3 | 3.9 GB | Low | C54 |
NVFP4 | 4 |
Copy-paste commands to run Llama 3 8B Instruct 32k v0.1 on your machine.
Run
lms load hf-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf && lms server startUpgrade options
RTX 3060 12GBBudget pickRaises estimated decode speed by about 27%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
RTX 5060 Ti 16GBBest valueRaises estimated decode speed by about 49%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 5070 12GBNVIDIA upgradeRaises estimated decode speed by about 127%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
Yes, RTX 2000 Ada Laptop 8GB can run Llama 3 8B Instruct 32k v0.1 with a C grade (Runs with offload). Expected decode speed: 38.3 tok/s.
Llama 3 8B Instruct 32k v0.1 (8B parameters) requires approximately 7.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 3 8B Instruct 32k v0.1 is Q4_K_M, which balances quality and memory efficiency.
On RTX 2000 Ada Laptop 8GB, Llama 3 8B Instruct 32k v0.1 achieves approximately 38.3 tokens per second decode speed with a time-to-first-token of 5055ms using Q4_K_M quantization.
For coding workloads, Llama 3 8B Instruct 32k v0.1 on RTX 2000 Ada Laptop 8GB receives a C grade with 38.3 tok/s and 19K context.
On RTX 2000 Ada Laptop 8GB, Llama 3 8B Instruct 32k v0.1 can safely use up to 19K 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-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf-on-rtx-2000-ada-laptop-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 19K |
4.5 GB |
| Medium |
| C53 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | C53 |
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 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.