llama.cpp
UnknownChecked the exact model config and the runtime architecture registry; no matching declared architecture was found. This is unknown, not evidence of incompatibility.
Checked 2026-10-27 00:08 UTC
Checked source ↗prism-ml · Released Sep 23, 2026 · observed 26 Oct 2026
Public-source support evidence
Ternary-Bonsai-2-9B-gguf
We have not found public evidence yet that Ternary-Bonsai-2-9B-gguf is listed in llama.cpp, Ollama, LM Studio, MLX or vLLM. We ran the Q2_0 GGUF on one Mac; see Hardware notes below.
prism-ml/Ternary-Bonsai-2-9B-gguf
Ternary-Bonsai-2-9B-gguf is an image text to text model published by prism-ml on Hugging Face. We have not written a summary yet; see the model card ↗.
View source model ↗SUPPORT EVIDENCE
Runtime status
Ollama
UnknownThe exact model and runtime-specific sources were checked; no positive model-level evidence was established.
Checked 2026-10-26 23:48 UTC
Version v0.40.11
Checked source ↗LM Studio
UnknownThe exact model and runtime-specific sources were checked; no positive model-level evidence was established.
Checked 2026-10-27 00:28 UTC
Checked source ↗MLX / mlx-lm
UnknownChecked the exact model config and the runtime architecture registry; no matching declared architecture was found. This is unknown, not evidence of incompatibility.
Checked 2026-10-27 00:08 UTC
Checked source ↗vLLM
UnknownChecked the exact model config and the runtime architecture registry; no matching declared architecture was found. This is unknown, not evidence of incompatibility.
Checked 2026-10-27 00:08 UTC
Checked source ↗MLX community uploads may target mlx-vlm; an upload observation does not establish mlx-lm compatibility.
MEMORY ESTIMATES
Which file should I grab?
The summary estimate of about 5 GB is the recommended Q2_0 quant (2.6 GB observed), from file size × 1.2 + 1.5 GB. Each card is the largest observed quant whose estimate fits that capacity.
16 GB capacity estimate
Which file should I grab?
Q2_0 · 2.6 GB observed · about 5 GB ↗About 5 GB using file size × 1.2 + 1.5 GB. Fit is an estimate; compatibility, speed, context length and hardware have not been tested. This estimates text weights only; vision projector and total vision memory are unknown.
24 GB capacity estimate
Which file should I grab?
Q2_0 · 2.6 GB observed · about 5 GB ↗About 5 GB using file size × 1.2 + 1.5 GB. Fit is an estimate; compatibility, speed, context length and hardware have not been tested. This estimates text weights only; vision projector and total vision memory are unknown.
32 GB capacity estimate
Which file should I grab?
Q2_0 · 2.6 GB observed · about 5 GB ↗About 5 GB using file size × 1.2 + 1.5 GB. Fit is an estimate; compatibility, speed, context length and hardware have not been tested. This estimates text weights only; vision projector and total vision memory are unknown.
64 GB capacity estimate
Which file should I grab?
Q2_0 · 2.6 GB observed · about 5 GB ↗About 5 GB using file size × 1.2 + 1.5 GB. Fit is an estimate; compatibility, speed, context length and hardware have not been tested. This estimates text weights only; vision projector and total vision memory are unknown.
128 GB capacity estimate
Which file should I grab?
Q2_0 · 2.6 GB observed · about 5 GB ↗About 5 GB using file size × 1.2 + 1.5 GB. Fit is an estimate; compatibility, speed, context length and hardware have not been tested. This estimates text weights only; vision projector and total vision memory are unknown.
192 GB capacity estimate
Which file should I grab?
Q2_0 · 2.6 GB observed · about 5 GB ↗About 5 GB using file size × 1.2 + 1.5 GB. Fit is an estimate; compatibility, speed, context length and hardware have not been tested. This estimates text weights only; vision projector and total vision memory are unknown.
256 GB capacity estimate
Which file should I grab?
Q2_0 · 2.6 GB observed · about 5 GB ↗About 5 GB using file size × 1.2 + 1.5 GB. Fit is an estimate; compatibility, speed, context length and hardware have not been tested. This estimates text weights only; vision projector and total vision memory are unknown.
512 GB capacity estimate
Which file should I grab?
Q2_0 · 2.6 GB observed · about 5 GB ↗About 5 GB using file size × 1.2 + 1.5 GB. Fit is an estimate; compatibility, speed, context length and hardware have not been tested. This estimates text weights only; vision projector and total vision memory are unknown.
Memory estimates are the observed file size plus fixed headroom (20% plus 1.5 GB runtime allowance); real needs also depend on context length, KV cache and runtime settings. Fit is an estimate; it does not establish runtime compatibility, speed, context length, or hardware performance. Text-weights-only estimates do not include a projector or total vision memory where unknown.
Terms: a quant is a lower-precision version of the weights; GGUF is a file format used by llama.cpp and compatible apps; MLX targets Apple silicon; vLLM is a model-serving engine for NVIDIA GPUs; the KV cache is stored attention state that grows with context length. Terms explained ↗
OBSERVED FILES
Quant uploads
Sizes and file groups come from the listed uploader repositories. A size is marked as an estimated fit only for a complete observed GGUF quant group.
| Uploader | Format & quant | Observed size | Estimated fit | Files |
|---|---|---|---|---|
| prism-mlprism-ml/Ternary-Bonsai-2-9B-gguf ↗Observed 27 Oct 2026 | GGUF · PQ2_0 | 2.4 GB | 16 GB, 24 GB, 32 GB, 64 GB, 128 GB, 192 GB, 256 GB, 512 GBEstimate only; requires the complete GGUF quant group. | 1 file |
| prism-mlprism-ml/Ternary-Bonsai-2-9B-gguf ↗Observed 27 Oct 2026 | GGUF · PTQ1_0 | 2.0 GB | 16 GB, 24 GB, 32 GB, 64 GB, 128 GB, 192 GB, 256 GB, 512 GBEstimate only; requires the complete GGUF quant group. | 1 file |
| prism-mlprism-ml/Ternary-Bonsai-2-9B-gguf ↗Observed 27 Oct 2026 | GGUF · Q2_0 | 2.6 GB | 16 GB, 24 GB, 32 GB, 64 GB, 128 GB, 192 GB, 256 GB, 512 GBEstimate only; requires the complete GGUF quant group. | 1 file |
CURATED TRACKING
Known issues
No curated issues verified for this snapshot. An empty list does not mean there are no bugs.
HARDWARE NOTES
We ran it on a Mac
We ran the Q2_0 GGUF (2.56 GB, prism-ml/Ternary-Bonsai-2-9B-gguf) on an Apple M4 Max Mac, 128 GiB unified memory, macOS 27.0.1, Metal with llama.cpp b11912 (upstream, 961c376) on 26 Oct 2026.
Decode: 91.7 tok/s on a short prompt, 90.3 tok/s on a long prompt (medians of 3 runs).
Peak memory: 3.79 GiB (llama-server footprint).
llama-bench: pp512 857.6 tok/s, tg128 93.9 tok/s.
Answer checks: 4 of 4 prompts passed (quick pass/fail sanity checks, not a quality benchmark).
This is the standard llama.cpp Q2_0 file from the same repo, not PrismML's PQ2_0 file. The PQ2_0 file crashed at warm-up in both PrismML llama.cpp builds we tried, so there are no PQ2_0 numbers here.
Thinking off (enable_thinking=false).
One machine, one run set: temperature 0, 8K context, 1 warm-up plus 3 runs per prompt. Short prompt is 23 tokens; long prompt is 1,431 tokens (each model counts with its own tokenizer). Weights were loaded from an external USB drive with mmap off; load time is not published for this run. Your speed will differ with hardware, context length and settings.
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