RX 7900 XT 20GBBest value
20 GB VRAM (+4)800 GB/s (+512)
C
Raises estimated decode speed by about 128%.98.4 tok/s decode
Raises estimated decode speed by about 128%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
dolphin 2.9.4 llama3.1 8b needs ~8.6 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~43 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
43.1 tok/s
TTFT
4494 ms
Safe context
142K
Memory
8.6 GB / 16.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 43.1 tok/s | 2451 ms | 142K |
| Coding | C | Runs well | 43.1 tok/s | 4494 ms | 142K |
| Agentic Coding | C | Runs well | 43.1 tok/s | 6536 ms | 142K |
| Reasoning | C | Runs well | 43.1 tok/s | 5311 ms | 142K |
| RAG | C | Runs well | 43.1 tok/s | 8170 ms | 142K |
Inference speed
Estimated decode speed (tokens/sec) for dolphin 2.9.4 llama3.1 8b 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 dolphin 2.9.4 llama3.1 8b (8B params) fits at each quantization level on RTX 4060 Ti 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 | C48 |
NVFP4 | 4 | 4.5 GB | Medium | C48 |
Q4_K_M | 4 | 4.9 GB | Medium | C49 |
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 |
Copy-paste commands to run dolphin 2.9.4 llama3.1 8b on your machine.
Run
lms load hf-bartowski--dolphin-2-9-4-llama3-1-8b-gguf && lms server startUpgrade options
Raises estimated decode speed by about 128%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
RTX A4500 20GBBudget pickRaises estimated decode speed by about 137%.
Adds memory headroom for longer context windows and future model growth.
~$2,000 MSRP
Yes, RTX 4060 Ti 16GB can run dolphin 2.9.4 llama3.1 8b with a C grade (Runs well). Expected decode speed: 43.1 tok/s.
dolphin 2.9.4 llama3.1 8b (8B parameters) requires approximately 8.6 GB of memory with Q4_K_M quantization.
The recommended quantization for dolphin 2.9.4 llama3.1 8b is Q4_K_M, which balances quality and memory efficiency.
On RTX 4060 Ti 16GB, dolphin 2.9.4 llama3.1 8b achieves approximately 43.1 tokens per second decode speed with a time-to-first-token of 4494ms using Q4_K_M quantization.
For coding workloads, dolphin 2.9.4 llama3.1 8b on RTX 4060 Ti 16GB receives a C grade with 43.1 tok/s and 142K context.
On RTX 4060 Ti 16GB, dolphin 2.9.4 llama3.1 8b can safely use up to 142K 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-bartowski--dolphin-2-9-4-llama3-1-8b-gguf-on-rtx-4060-ti-16gb" 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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