To run a 27B model on less RAM, quantize it — distillation is for training a smaller student, and PRISM is a robot-planning paper, not a toolkit

Asked:

“What are the best OS for distilling known models? Prism? I heard there are others to try and get the latest 27b models on lower RAM”

An 11-row decision matrix compares five open-source distillation/training tools and five low-memory quantized-inference stacks, plus one scope check on the PRISM robot-planning paper. The 11 rows are drawn from seven independent hosts, and each row has its own source URL. Memory figures are in GB of weights unless stated; KV cache and runtime overhead are additional.

RAM planning for a 27B-class model. Current 4-bit GGUF weights land around 16–19 GB (one Qwen3.8-27B Q4_K_M listing is 17.44 GB per the quant-size range cited). Lower-bit files shrink further but lose quality. Weights are not the whole budget: KV cache, context, runtime and the OS add on top. 16 GB machines are generally too tight for a dense 27B; ~24 GB can work with aggressive quantization and short context; 32 GB is the practical floor.

If you really do want to distill

First choice · official docs

TRL Distillation Trainer / GKD

Hugging Face's on-policy knowledge distillation with generalized Jensen-Shannon divergence loss; supports LoRA, gradient checkpointing, DeepSpeed/FSDP, and huge teachers (100B+) on an external vLLM server. Checkpoints reload with from_pretrained().

NVIDIA-scale · Apache-2.0

NVIDIA ModelOpt

Logit-based KD and quantization-aware distillation (QAD) via Megatron-Bridge — the pick when the distilled student must ship quantized on NVIDIA infrastructure. Exports Megatron and Hugging Face formats.

Lower-level PyTorch

torchtune

PyTorch-native KD recipes with LoRA/QLoRA, FSDP2 and QAT+LoRA; model support explicitly includes Gemma 2 27B and up to 405B. SDFT is an interesting self-distillation option but not the default start.

Findings

TRL's Distillation Trainer can distill from teachers too big for your training GPUs — even 100B+ — by hosting the teacher on an external vLLM server. huggingface.co

A 32–36 GB Mac fits a 4-bit 27B (IQ4_XS/Q4_K_M, ~15–16.5 GB) with a real context window if the MLX memory limit is set explicitly. aixcove.com

Unsloth's dynamic GGUFs span roughly 56 GB at full BF16 down to 7–8 GB at 1-bit, with the practical 4-bit sweet spot at 16–19 GB of combined RAM+VRAM. intuitionlabs.ai

NVIDIA ModelOpt is the one distillation stack that trains a quantized student directly (quantization-aware distillation), so the shrunk model deploys as-is. github.com

All 11 rows

Tool	Category	Best for	Memory guidance	Model support	Source

Decision matrix of 11 rows gathered from 11 pages on 7 independent hosts: 5 open-source distillation/training tools, 5 quantized-inference options for 27B-class models, and 1 scope check on the PRISM robot-planning paper. Each row carries its own source URL; no URL backs more than one row. Memory figures are approximate weight footprints in GB; KV cache, context and runtime overhead are additional. Long method and deployment details were shortened in the table — hover a box in the diagram or follow a source link for the full text. Official documentation (huggingface.co, github.com project pages) is labeled "official docs"; hardware figures from aixcove.com, intuitionlabs.ai, kingy.ai and grokipedia.com are third-party guidance. MLX-Flash is experimental.

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