Can I Run Ternary Bonsai 2 on NVIDIA GeForce RTX 4060?
Written by Jakub Rusinowski · Last updated October 15, 2026
Yes, but it's tight — Ternary Bonsai 2 27B at PTQ1_0 ternary needs 7.3 GB at 8K context (5.9 GB weights + 1.4 GB KV cache/overhead), leaving ~0.7 GB of the NVIDIA GeForce RTX 4060's 8 GB, at ~37 tok/s (est.), with room for up to 16K context.
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NVIDIA GeForce RTX 4060 Specs
| VRAM | 8 GB |
| Memory Bandwidth | 272 GB/s |
Ternary Bonsai 2 27B on the NVIDIA GeForce RTX 4060: VRAM by quantization
| Quant | VRAM needed | Fits 8 GB? | Max context | Whole PC |
|---|---|---|---|---|
| PQ2_0 ternary | 8.5 GB | ✗ No | — | — |
| PTQ1_0 ternary | 7.3 GB | ✓ Yes | 16K | — |
Assumes an 8K-token context with an f16 KV cache. A longer window needs more; a quantized KV cache needs less. “Max context” is the largest window that still fits in 8 GB. Figures are estimates from parameter count, quantization and memory bandwidth — the analyzer lets you tune KV-cache quant and context.
Context costs VRAM too. At PTQ1_0 ternary on the NVIDIA GeForce RTX 4060: 8K 7.3 GB · 32K 8.9 GB ✗ · 128K 15.3 GB ✗. Past 32K it no longer fits 8 GB — a q8_0 KV cache buys roughly half of that back, which the calculator will price for you.
Ternary Bonsai 2 Sizes That Fit the NVIDIA GeForce RTX 4060 (at 8K context)
| Ternary Bonsai 2 27B | PTQ1_0 ternary · 7.3 GB at 8K context · ~37 tok/s (est.) |
| Ternary Bonsai 2 9B | PTQ1_0 ternary · 3.1 GB at 8K context · ~94 tok/s (est.) |
| Ternary Bonsai 2 4B | PTQ1_0 ternary · 2 GB at 8K context · ~154 tok/s (est.) |
| Ternary Bonsai 2 2B | PTQ1_0 ternary · 1.3 GB at 8K context · ~244 tok/s (est.) |
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FAQ
Is the NVIDIA GeForce RTX 4060 enough to run Ternary Bonsai 2 locally?
Yes, but it's tight — Ternary Bonsai 2 27B at PTQ1_0 ternary needs 7.3 GB at 8K context (5.9 GB weights + 1.4 GB KV cache/overhead), leaving ~0.7 GB of the NVIDIA GeForce RTX 4060's 8 GB, at ~37 tok/s (est.), with room for up to 16K context.
What's the best Ternary Bonsai 2 size for the NVIDIA GeForce RTX 4060?
Ternary Bonsai 2 27B at PTQ1_0 ternary quantization needs 7.3 GB at 8K context (5.9 GB weights + 1.4 GB KV cache/overhead), estimated ~37 tokens/sec, up to 16K context.
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VRAM Tier
Troubleshooting
- CUDA out of memory — why it happens and how to fix it
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Buying Guide
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