RTX 3060 12GBBudget pick12 GB VRAM (+4)360 GB/s (+136)
C
Raises estimated decode speed by about 26%.48.7 tok/s decode
Raises estimated decode speed by about 26%.
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 5050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~39 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
38.6 tok/s
TTFT
5021 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 | 38.6 tok/s | 2739 ms | 19K |
| Coding | C | Runs with offload | 38.6 tok/s | 5021 ms | 19K |
| Agentic Coding | D | Very compromised (needs ~0.4 GB host RAM) | 24.5 tok/s | 11480 ms | 19K |
| Reasoning | C | Runs with offload | 38.6 tok/s | 5934 ms | 19K |
| RAG | D | Very compromised (needs ~0.4 GB host RAM) | 24.5 tok/s | 14350 ms | 19K |
Inference speed
Estimated decode speed (tokens/sec) for Llama 3 8B Instruct 32k v0.1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| RTX 5090 32GB | 32 GB | Q4_K_M | 112.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 112.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 95.1 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 90.2 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 77.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 76.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 76.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 49.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 48.7 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 45.1 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 40.7 | Offloads |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 39.6 | Fits |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on RTX 5050 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 | 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 |
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 26%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
RTX 5060 Ti 16GBBest valueRaises estimated decode speed by about 47%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 5070 12GBNVIDIA upgradeRaises estimated decode speed by about 125%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
Yes, RTX 5050 8GB can run Llama 3 8B Instruct 32k v0.1 with a C grade (Runs with offload). Expected decode speed: 38.6 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 5050 8GB, Llama 3 8B Instruct 32k v0.1 achieves approximately 38.6 tokens per second decode speed with a time-to-first-token of 5021ms using Q4_K_M quantization.
For coding workloads, Llama 3 8B Instruct 32k v0.1 on RTX 5050 8GB receives a C grade with 38.6 tok/s and 19K context.
On RTX 5050 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.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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-5050-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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