Model × hardware check
Can Bonsai 2 run on NVIDIA GeForce RTX 5090 · 32 GB?
Yes, this is a comfortable estimate at 8,192 tokens. a true ternary 27B model whose packed files are smaller than many 7-9B Q4 builds The RTX 5090's 32 GB frame buffer is the largest consumer tier in this catalog, with 1792 GB/s of memory bandwidth.
Short answer
Comfortable fit
At least 20% memory headroom remains after weights, KV cache, and runtime overhead.
- Available VRAM
- 32 GB
- Estimated requirement
- 9.2 GB
- Headroom
- +22.8 GB
- Fit ratio
- 346%
- Recommended quantization
- PQ2_0
- Context ceiling
- 262,144 tokens
- Speed estimate
- Estimate · Fast relative band
What the numbers say
The selected Bonsai 2 package uses 7.2 GB of weight files. The 8K KV cache estimate is 0.5 GB, and the formula adds 1.5 GB of runtime overhead. That produces a 9.2 GB requirement against 32 GB of VRAM.
The first 32 GB consumer-class target in this catalog. The extra capacity is most useful for larger quantized models or longer contexts.
The context ceiling shown above is the point where the same formula reaches the machine's capacity. It is not a promise that the backend can allocate that full context after the operating system and other applications take their share.
Quantization trade-offs at 8K
| Quantization | Weight files | Bits / weight | 8K requirement | Fit on this machine | Context ceiling | Speed estimate | Ollama |
|---|---|---|---|---|---|---|---|
| PTQ1_0 | 5.95 GB | 1.75 | 8.0 GB | Comfortable fit · 401% | 262,144 tokens | Estimate · Fast relative band | No verified tag |
| PQ2_0 | 7.21 GB | 2.13 | 9.2 GB | Comfortable fit · 346% | 262,144 tokens | Estimate · Fast relative band | No verified tag |
| Q2_0 | 7.63 GB | 2.25 | 9.7 GB | Comfortable fit · 331% | 262,144 tokens | Estimate · Fast relative band | ollama run bonsai-2:27b-q2_0 |
This model uses true ternary language weights at roughly 1.75-2.25 bits per weight after scales and metadata. The table uses each published GGUF size directly; it does not replace Bonsai's ternary file with a generic Q4 estimate.
Run commands
llama-cli -hf prism-ml/Ternary-Bonsai-2-27B-gguf --hf-file Ternary-Bonsai-2-27B-PQ2_0.gguf -c 8192 -ngl 99 -p "Your prompt"mlx_lm.generate --model prism-ml/Ternary-Bonsai-2-27B-mlx-2bit --prompt "Your prompt" --max-tokens 8192Commands are examples, not a guarantee that the selected quantization tag is available in every runtime. Confirm the package name and license on the linked model page.
If it does not fit
This pairing already clears the comfort threshold
The current estimate leaves +22.8 GB. A longer context or another GPU application can reduce that margin, so verify free memory before relying on it.
Low-cost setup upgrades
Useful upgrades around NVIDIA GeForce RTX 5090 · 32 GB
These are optional setup improvements, not a claim that an accessory can increase GPU or unified-memory capacity.
External NVMe SSD for model storage
Keeps large GGUF and MLX files off the boot drive. Fast storage reduces load time, but it does not increase the memory available for inference.
Search current listings on AmazonDesktop RAM upgrade kit
Useful for MoE CPU offload and keeping the operating system out of VRAM. Match the motherboard's memory type, slot layout, capacity limit, and validated part numbers.
Search current listings on AmazonGPU support bracket
A lower-cost mechanical upgrade for heavy three-slot cards. Check clearance, mounting height, and whether the bracket blocks fans or case panels.
Search current listings on AmazonAs an Amazon Associate I earn from qualifying purchases. Links are sponsored search links and do not change the price you pay. Affiliate disclosure.
Where this estimate can break
- PTQ1_0 and PQ2_0 require Prism ML's llama.cpp fork. Q2_0 runs in stock llama.cpp b11556 or newer; older builds can load it without warning and produce garbage.
- The calculator models the 27B flagship. The 2B, 4B, and 9B variants are listed separately on this page and use their own files and vision towers.
- The vision tower is optional for text inference but adds about 0.63 GB when image input is enabled.
- The 262K context remains impractical on an 8-12 GB machine even though the ternary weights themselves are unusually small.
- Other GPU applications, display buffers, and the operating system reduce usable memory.
- Longer prompts, concurrent requests, and a different backend can raise peak memory.
Compare this pairing
FAQ
Questions about this pairing.
How much memory does Bonsai 2 need on NVIDIA GeForce RTX 5090 · 32 GB?
At an 8K context with PQ2_0, the estimate is 9.2 GB: 7.2 GB of weights, 0.5 GB of KV cache, and 1.5 GB of runtime overhead. The available VRAM is 32 GB.
What context length can NVIDIA GeForce RTX 5090 · 32 GB handle with this model?
The calculated ceiling is 262,144 tokens for this quantization, capped by the model's native 262,144-token context. Actual usable context can be lower after the OS, other applications, and backend workspace.
How does quantization change the memory requirement on NVIDIA GeForce RTX 5090 · 32 GB?
At 8,192 tokens, the published variants calculate as follows: PTQ1_0: 8.0 GB, comfortable fit; PQ2_0: 9.2 GB, comfortable fit; Q2_0: 9.7 GB, comfortable fit. A lower-bit file can move a result from too large to tight, but quality and kernel support may differ.
What can I run if Bonsai 2 does not fit on NVIDIA GeForce RTX 5090 · 32 GB?
This selection already clears the comfort threshold. If you later increase the context, monitor the ceiling of 262,144 tokens; smaller alternatives include Flint 40B, NVIDIA Nemotron 3.5 Bolt E4B, LFM3-1.7B.
Is the speed estimate for Bonsai 2 on NVIDIA GeForce RTX 5090 · 32 GB measured?
No. Fast relative band is a heuristic band derived from 27.36B active parameters and 1792 GB/s published memory bandwidth. Backend kernels, batch size, thermals, and CPU offload can change actual tokens per second.