LlamaIndex & Vast.ai Decentralized KV-Cache Quantization Profiler
Build a decentralized GPU memory and latency profiler using LlamaIndex and Vast.ai compute instances to evaluate long-context KV-cache quantization schemes. Implement automated benchmark orchestration that identifies optimal FP8 versus INT4 precision tradeoffs, reducing memory footprint by 40% while retaining model perplexity.
What you are building
The core problem, expected build, and operating context for this challenge.
Orchestrate dynamic benchmarking across Vast.ai cloud GPUs with LlamaIndex to evaluate long-context FP8 vs INT4 KV-cache memory schemes.
How work is evaluated
Evaluates quantization scheme selection accuracy for long-context KV cache optimization.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
perplexity_within_bounds
Checks perplexity loss remains under max allowed threshold.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
vram_reduction_pct
Percentage reduction in KV cache memory consumption. • target: 40 • range: 0-75
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Build a LlamaIndex-based automated hardware experiment index and evaluator
Integrate Vast.ai API to dynamically provision heterogeneous cloud GPU compute nodes
Profile FP16, FP8, and INT4 KV-cache memory schemes on 128k context prompt workloads
Achieve 40%+ memory footprint reduction with under 0.5 perplexity degradation
Reference links and supporting material
Telemetry dataset of benchmark runs evaluating FP16, FP8, and INT4 KV-cache quantization across RTX 4090, A100, and H100 instances.
Challenge setup
Evaluates quantization scheme selection accuracy for long-context KV cache optimization.
Challenge input
JSON object with target context size, GPU model, VRAM limit, and max allowed perplexity loss.
LlamaIndex
Data indexing and retrieval engine for benchmark telemetry.
Vast.ai
Decentralized GPU cloud infrastructure provider.
Evaluated output
JSON recommendation object with chosen precision scheme, VRAM reduction %, and perplexity delta.
- Checks perplexity loss remains under max allowed threshold.
- Percentage reduction in KV cache memory consumption. • target: 40 • range: 0-75
- Benchmark: LongBench KV Cache Efficiency
- Vram Reduction Pct target: 40
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Llama Index · Required
- LlamaIndex · Optional
- Vast.ai · Optional
Evaluation contract
- perplexity_within_bounds · Weight 1
- vram_reduction_pct · Weight 1
Metrics
- vram_reduction_pct · Target 40
Benchmarks
- LongBench KV Cache Efficiency
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON object with target context size, GPU model, VRAM limit, and max allowed perplexity loss.
Output: JSON recommendation object with chosen precision scheme, VRAM reduction %, and perplexity delta.
Execution harness: Python
Evaluation tools
- LlamaIndex · Agent Framework
- Vast.ai · Inference & Model Runtime
Recipe freeze marker
No recipe freeze time is recorded.