hyper-stack-4j

Distributed Java-native LLM inference engine. Runs large language models across a cluster of commodity GPUs. No Python, no GIL, no Spring.

16 × 4 GB GPUs = 64 GB total VRAM at a fraction of the cost of a single high-VRAM card.

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Status

All modules build and all tests pass. Verified end-to-end with TinyLlama-1.1B-Chat-v1.0.Q4_K_M.gguf on a 3-node CPU cluster.

Session 9 — multi-turn KV cache reuse. Each conversation turn now skips re-prefilling previously processed tokens. Turn latency is constant per turn instead of growing O(N) with history length.

you> hey there, my name is Dima, nice to meet you!
bot> Greetings! Nice to meet you too.
[37 tokens · 7342 ms · FLOAT16]
you> what is my name?
bot> Your name is Dima.
[11 tokens · 8103 ms · FLOAT16] ← flat, not growing

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Architecture

[Client] REST (Javalin) / gRPC streaming
↓
[Coordinator]
├── GgufTokenizer (BPE from GGUF metadata)
├── ChatTemplateFormatter
├── RequestScheduler (virtual threads, CompletableFuture)
├── Sampler (temperature / top-k / top-p / rep. penalty)
├── KVCacheManager (GPU tier + CPU tier + PrefixCache trie)
└── GenerationLoop (prefill + decode + session KV reuse)
│
│ gRPC (activations — FLOAT16/INT8/FLOAT32)
│
┌──────────────────────────────────────┐
│ Node 1 Node 2 Node 3 ... │ 10/25 GbE
│ L 0–7 L 8–14 L 15–21 │
│ +embed +output proj │
└──────────────────────────────────────┘

Each node runs CpuForwardPassHandler — full LLaMA-family transformer math in pure Java, parallel matVec across all CPU cores. GpuForwardPassHandler (JCuda) in progress.

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Quick Start

# Download model (637 MB, CPU-only)
wget https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf
# Build
mvn clean package -DskipTests
# Interactive REPL — all nodes in one JVM (dev)
./run.sh console --model-path /path/to/model.gguf
# 3-node cluster — forked JVM nodes, real gRPC (production)
./run.sh cluster --model-path /path/to/model.gguf
# Real-model smoke test — 6 checks, exits 0/1
./run.sh live --model-path /path/to/model.gguf

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run.sh

Production launcher. Requires a JDK and pre-built jars from target/. No Maven.

Command	Description
console	In-process REPL, single JVM, no forking
cluster	3-node cluster, forked JVMs, real gRPC
live	6 automated real-model checks, exits 0/1

Flags (console and cluster):

Flag	Default	Description
--model-path PATH	—	Path to GGUF file (required)
--dtype FLOAT32|FLOAT16|INT8	FLOAT16	Activation wire format
--max-tokens N	200	Max tokens per response
--temperature F	0.6	Sampling temperature
--heap SIZE	4g	JVM heap, e.g. 8g for 7B models
--verbose	—	Show gRPC and node logs

Environment overrides:MODEL_PATH, DTYPE, MAX_TOKENS, TEMPERATURE, HEAP, NODES, JAVA_HOME.

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Modules

Module	Contents
api	OpenAPI 3.0 spec, JAX-RS interfaces, inference.proto
registry	NodeDescriptor, ShardPlanner, ShardMap
coordinator	GenerationLoop, RequestScheduler, FaultTolerantPipeline, Javalin REST, SSE
kvcache	KVCacheManager, GpuKVCache, CpuKVCache, PrefixCache
tokenizer	GgufTokenizer (SentencePiece BPE), ChatTemplate, StubTokenizer
node	CpuForwardPassHandler, GgufReader, LlamaConfig, ActivationCodec
sampler	Temperature, top-k, top-p, repetition penalty — pure Java
health	Health monitor, circuit breakers (Resilience4j)
player	ConsoleMain REPL, ClusterHarness, ProcessPipelineClient, ChatHistory
integration	InProcessClusterIT, ThreeNodeClusterIT, ModelLiveRunner

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Supported Models

Any GGUF file with a LLaMA-compatible architecture. Tested:

Model	File size	RAM
TinyLlama-1.1B-Chat Q4_K_M	637 MB	~2 GB
Mistral-7B-Instruct Q4_K_M	4.1 GB	~6 GB
Llama-3.2-8B-Instruct Q4_K_M	4.9 GB	~8 GB
Llama-3.1-70B-Instruct Q4_K_M	40 GB	16 × 4 GB nodes

Quantisation types: F32, F16, BF16, Q8_0, Q4_0, Q4_K, Q6_K.

Chat templates: llama3, mistral, gemma, tinyllama/zephyr, chatml (default). Template derived automatically from the GGUF file name.

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Build and Test

mvn clean package -DskipTests # build — produces shade jars
mvn test -pl tokenizer,node,coordinator,sampler,kvcache,health,registry,player
# unit tests — no model file needed
mvn verify -pl integration # integration tests — forks 3 JVM nodes (stub mode)
./run.sh live /path/to/model.gguf # real-model smoke test

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Performance

Session	Change	ms / 10 tokens
5	Baseline (FLOAT32, serial matVec)	~34,891 ms
6	Parallel matVec + FLOAT16 default	~3,802 ms (9×)
9	Session KV cache — turn latency now flat	~7,000–8,000 ms / turn

Session 9 turn latency grows with new tokens per turn only, not with total history length.

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Key Design Decisions

• No Python, no llama.cpp subprocess. JVM reads GGUF binary directly and runs the transformer end to end.
• No Spring Boot. Javalin for REST.
• Pipeline parallelism over tensor parallelism — LAN-friendly, no InfiniBand required.
• Separate data plane (gRPC activations) from control plane (Hazelcast state).
• GGUF tokenizer loaded from model metadata — no separate tokenizer.model file.
• Stub mode — cluster boots in seconds without a model file; all integration tests run stub.
• Two ActivationDtype enums by design: protobuf-generated for wire, domain enum for application code.

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Requirements

• JDK 25+
• Maven 3.9+
• CUDA 12.x (GPU nodes only — not required for CPU mode)

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License

Apache 2.0