mledgeoptimization

Compact AI Assistants on Tiny Hardware: Pruning, Quantization, and Latency Tricks

UUnknown

2026-02-15

10 min read

Advanced, hands-on techniques to run compact LLMs on Raspberry Pi 5 + AI HAT+: pruning, GPTQ/AWQ quantization, operator fusion, and runtime tips.

Hook: Ship LLM-powered features on a Raspberry Pi — without waiting for the cloud

If you're a developer or systems engineer frustrated by cloud latency, runaway costs, and flaky network conditions, running compact LLMs at the edge is no longer wishful thinking. In 2026 the Raspberry Pi 5 combined with the new AI HAT+ family and a maturing stack of quantizers and compilers means you can deploy meaningful generative features locally — but only if you apply advanced engineering: aggressive pruning, state-of-the-art quantization (GPTQ/AWQ-era tools), operator fusion, and the right runtime choices.

What this guide gives you

Concrete, step-by-step optimizations to squeeze LLMs onto Pi 5 + AI HAT+ (and similar tiny hardware)
Hands-on examples: pruning recipes, quantization commands, TVM/ONNX fusion notes, and latency tricks
Runtime trade-offs for ARM and emerging RISC-V edge silicon (including recent 2025–2026 platform shifts)

Context: why 2025–2026 changes matter

Late 2025 and early 2026 brought three forces that change edge AI engineering:

Hardware: Raspberry Pi 5 + AI HAT+ accelerators enable offload of neural kernels to on-board NPUs, making generative workloads feasible at low price points.
Compiler & quantization advances: GPTQ and AWQ families matured into robust toolchains and were integrated into TVM/ONNX pipelines, enabling high-quality 3–4-bit inference with small accuracy loss.
SiFive + NVLink work (announced in early 2026) point to more RISC-V SoCs gaining GPU/NPU interconnects — expect richer edge offload options and vendor-optimized runtimes.

Start by defining constraints — measure first

Before pruning or quantizing, benchmark your baseline so you can measure gains. Key metrics:

Memory footprint: peak RAM and VRAM usage while generating
Latency: time-to-first-token (TTFT) and tokens/sec
Quality: perplexity or targeted task accuracy after compression

Use a minimal benchmark harness to generate 128 tokens and measure wall-clock times. Example Python harness (replace model runtime calls with your runtime):

import time
from model_runner import generate  # placeholder

start = time.perf_counter()
out = generate(prompt='Hello edge world', max_new_tokens=128)
elapsed = time.perf_counter() - start
print('TTFT+gen:', elapsed)

1) Pruning: the first order of compression (safe, iterative, measurable)

Pruning reduces model parameter count and memory. For edge LLMs you should favor structured pruning where possible (remove attention heads, neurons, or blocks) over unstructured magnitude pruning — structured pruning yields better compute and kernel simplification on NPUs.

Recommended pruning workflow

Select a baseline small model or distillation (1–7B models are realistic targets for Pi+HAT).
Apply head and FFN-node structured pruning at low rates (10–30%) and fine-tune for a few epochs on the most relevant data.
Iterate: prune 10% per pass -> fine-tune -> validate until latency/memory goals are met.
If you need more, apply magnitude-based pruning as a last step, then do a short retrain.

Code: head pruning with PyTorch (concept)

import torch
from torch.nn.utils import prune

# assume model.transformer.h contains attention modules
for layer in model.transformer.h:
    attn = layer.attn
    # remove heads by zeroing corresponding projection rows
    # Here we do structured pruning by masks per head
    mask = torch.ones_like(attn.q_proj.weight)
    # compute head indices to zero (example: drop head 3)
    # apply mask and reassign weights
    attn.q_proj.weight.data *= mask

# fine-tune shortly after pruning

Note: many frameworks (Hugging Face Transformers) include head masking utilities; use them when available so you keep compatibility with model code paths.

2) Quantization: where you get the biggest bang for edge

By 2026, optimized 3–4-bit quantizers (GPTQ/AWQ derivatives) are the de-facto standard for running 7B models on tiny hardware. They maintain usable quality and reduce memory/compute significantly. Choose the right technique by target runtime:

Int8 PTQ — simplest, supported everywhere (TFLite, ONNX Runtime). Good for small to medium models.
GPTQ / AWQ — post-training quantization that preserves attention/MLP accuracy and produces 3–4-bit models often used with GGML/llama.cpp-style runtimes.
Quantization-aware training (QAT) — if you can afford fine-tuning, QAT gives best final accuracy at low bitwidths.

Practical commands (examples)

Example: use a GPTQ-style quantizer (gptq-cli) — typical command pattern in 2025–2026 toolchains:

# quantize a PyTorch checkpoint into a 4-bit GPTQ file
gptq-cli quantize --model path/to/model.bin --out model.4bit.gptq --bits 4 --act-order

For llama.cpp/ggml-style deployment, convert then quantize (on a workstation) and copy quantized file to Pi:

# convert HF -> ggml then quantize (pseudo-commands)
python convert_to_ggml.py --hf-model HF_MODEL_ID --out model.ggml
./quantize model.ggml model.q4_0 4

3) Operator fusion & kernel-level optimizations

Operator fusion reduces memory copies and increases cache locality. On tiny hardware, each saved memcpy and kernel launch reduces latency. Use compilers like Apache TVM, ONNX Runtime with fusion passes, or vendor SDKs that support fused attention+FFN kernels.

Why fusion matters

Reduces DRAM traffic: attention + projection fused saves one read/write per token.
Allows kernel reordering to match NPU microkernels and SIMD lanes (NEON / RVV).
Enables mixed precision kernels that exploit 4-bit inner loops while keeping accumulation in FP16/FP32.

TVM flow (conceptual)

Use TVM Relay to apply pattern-based fusion for attention. High-level steps:

Import your model into Relay (from ONNX or PyTorch)
Apply Relay passes: operator fusion, layout transform, target-specific lowering
Build with targets: arm_cpu (NEON), hexagon, or custom NPU codegen
Deploy runtime module to Pi + AI HAT+, run benchmarks

import tvm
from tvm import relay

mod, params = relay.frontend.from_onnx(onnx_model)
# apply fusion and optimizations
mod = relay.transform.FuseOps()(mod)
# build for ARM with NEON
with tvm.transform.PassContext(opt_level=3):
    lib = relay.build(mod, target='c -device=arm_cpu', params=params)

4) Runtime choices: pick for locality and operator coverage

Which runtime you pick depends on hardware and model format. Short guide:

llama.cpp / GGML: excellent for quantized 4-bit models on CPU; minimal runtime and well-optimized for NEON.
ONNX Runtime: use when you want broad operator coverage and vendor EPs (NPU/GPU) — good for TFLite/ONNX pipelines.
TVM runtime: best when you need fused, target-specific kernels; involves more build complexity but yields best latency.
Vendor SDKs: if AI HAT+ provides a C SDK or runtime, it may deliver the best throughput by exposing the NPU — use it for hot paths and fallback to CPU for unsupported ops. Also evaluate vendor support and trust using vendor telemetry assessments like trust score reviews.

RISC-V specifics

RISC-V edge silicon in 2026 is increasingly common. Two considerations:

Check for RVV (vector extension) optimized kernels — if present, runtimes with RVV codegens (TVM or vendor) can outperform ARM NEON.
Interconnects like NVLink Fusion (industry moves in 2026) hint at future setups where a RISC-V host orchestrates a local GPU/NPU — design your deployment to allow offload and sharding.

5) Latency tricks that add up

Beyond model-level compression, use these practical latency optimizations:

Token caching: always reuse KV-caches between tokens and save them in memory format friendly to your runtime (aligned, pinned)
Batching & token packing: pack multiple short requests into a batch to amortize attention cost
Early exit / dynamic depth: conditionally use fewer layers for easy prompts (fast-path)
Async IO & pipelining: overlap token generation, post-processing, and network I/O
Memory-map quantized files: mmap the quantized model to avoid loading and duplicating memory
Use integer math where possible: NPU microkernels often favor integer ops; align quantization to exploit them

Small example: mmap load

with open('model.q4', 'rb') as f:
    import mmap
    mm = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
    # pass mm to runtime loader if supported

6) Memory sharding and hybrid offload

If the model still doesn't fit local memory, consider:

Layer sharding: keep hot layers on NPU, cold layers on CPU — controlled by runtime scheduler
On-demand paging: stream blocks from a fast NVMe or SD cache (trade latency spikes for reduced RAM)
Hybrid split: run attention on NPU, MLP on CPU (or vice versa) depending on operator support

7) Validation: keep quality acceptable

Every compression step can reduce model quality. Validate using:

Perplexity on representative text
Task-specific metrics (QA F1, intent detection accuracy)
Human evals when possible — small shifts in generation style can matter more than raw perplexity

Maintain a simple regression test harness so that each pruning/quantization pass is gated by a maximum allowable drop in chosen metrics.

8) Real-world recipe: Pi 5 + AI HAT+ deployment checklist

Choose a starting model (distilled 3–7B or smaller). Prefer models with community-available GPTQ/AWQ checkpoints.
Prune structured elements (10–30% per iteration) and run a short fine-tune on representative data.
Quantize using GPTQ/AWQ to 3–4 bits on a workstation. Validate quality and tokens/sec.
Convert to your runtime format (GGUF/ggml, ONNX, or TVM module).
Use TVM or vendor SDK to generate fused kernels targeting the AI HAT+ NPU. If unsupported, use NEON-optimized llama.cpp binaries.
Deploy quantized file with mmap loading; pre-warm caches and KV stores for common prompts.
Monitor TTFT and tokens/sec in production and keep a rollback mechanism to the previous model if quality drifts.

9) Example: iterative pipeline commands (workstation -> Pi)

# 1) Prune+Fine-tune (on workstation)
python prune_and_finetune.py --model hf/my-small-model --prune-heads 0.2 --epochs 3 --out pruned.pt

# 2) GPTQ quantize
gptq-cli quantize --model pruned.pt --out pruned.4bit.gptq --bits 4

# 3) Convert to runtime format (example ggml)
python convert_to_ggml.py --input pruned.4bit.gptq --out pruned.ggml

# 4) Transfer and run on Pi
scp pruned.ggml pi@pi5:/home/pi/models/
ssh pi@pi5 './run_llm.sh /home/pi/models/pruned.ggml'

10) Observability & production hardening

Edge deployments need different observability than cloud. Track:

Tail latency (p95/p99) — pruning and offload can create outliers
Thermal throttling — Pi+HAT+ thermal profiles affect long-run throughput
Memory spikes during certain prompts — guard with circuit breakers

Pro tip: add a lightweight watchdog that returns a cached answer or proxy to cloud inference on p95+p99 events — better UX than long stalls.

Future-proofing: where edge LLMs go in 2026–2027

Expect three trends to shape the next 12–24 months:

RISC-V acceleration: more NPUs paired with RISC-V hosts, plus interconnects like NVLink Fusion, will enable richer on-device offload topologies.
Compiler automation: TVM-style toolchains will incorporate GPTQ/AWQ quantization passes and produce fused kernels automatically.
Model co-design: tiny LLM architectures that accept lower-precision attention will appear — designed to work with 3–4-bit pipelines from the start.

Final checklist — what to measure after each change

TTFT and tokens/sec
Peak RAM and NPU memory usage
Perplexity and task-specific accuracy
Thermal and CPU throttling behavior

Closing: make edge LLMs reliable, not just possible

Deploying compact LLMs on Raspberry Pi 5 + AI HAT+ is a multi-layer engineering effort. Combine structured pruning, modern GPTQ/AWQ quantization, operator fusion via compilers like TVM or vendor SDKs, and smart runtime choices to win on latency and quality. The 2025–2026 advances in quantizers and the rising RISC-V ecosystem mean that edge LLMs will keep getting better — but only if you treat the model and runtime as a single system to optimize.

Actionable takeaways

Measure first: baseline latency, memory, and accuracy.
Prune structured components and fine-tune; then apply GPTQ/AWQ 3–4-bit quantization.
Use TVM or vendor fusion to reduce memory copies — this often beats naive quantization alone.
Exploit token caching, mmap loading, and batching to reduce observed latency for users.
Design fallbacks for p95/p99 spikes — hybrid cloud fallback is pragmatic.

Ready to get hands-on? Clone our reference repo with pruning scripts, GPTQ integration examples, and TVM build pipelines tailored for Pi 5 + AI HAT+. We keep the repo updated with 2026 toolchain tips and RISC-V notes.

Call to action

Try the pipeline: prune a 7B model, quantize to 4-bit, and deploy on a Pi 5 + AI HAT+ today — then share your latency and quality numbers. Join thecode.website community for step-by-step scripts, prebuilt images, and a weekly update on edge inference trends (RISC-V, NVLink, GPTQ/AWQ integrations) so you can ship faster and with confidence.

Unknown

Contributor

Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.