Releases·Tool Release
Show HN: Lifeboat, 2-6x more concurrent agent sessions per GPU, no quantization
Show HN: Lifeboat, 2-6x more concurrent agent sessions per GPU, no quantization
1 engagement1 sourceWed, Sep 23, 2026, 08:32 PM
AI Overview
- Lifeboat's GitHub project claims 2-6x more concurrent agent sessions per GPU without quantization
- The tool targets agent workloads, sharing GPU capacity across multiple simultaneous sessions
- Announced as a Show HN post on 2026-09-23 linking to the project's GitHub repository
Entities
Related
Social·Community
CTO benchmarks 2×4090 llama.cpp agent concurrency: soft cap 5 @ 64k, hard cap 9, prefill stuck ~1,500 tok/s
A nonprofit CTO running a local Qwen coding-agent stack on 2×RTX 4090s benchmarked four models and three quantizations over three weeks to determine practical concurrency limits. The data caps out at a soft limit of 5 concurrent agents at 64k context and a hard cap of 9, relevant to practitioners building local multi-agent setups.
A nonprofit CTO running a local Qwen coding-agent stack on 2×RTX 4090s benchmarked four models and three quantizations over three weeks to determine practical concurrency limits. The data caps out at a soft limit of 5 concurrent agents at 64k context and a hard cap of 9, relevant to practitioners building local multi-agent setups.
33 engagement1 sourceredditTue, Sep 8, 2026, 10:21 PM
Developments·Benchmark
User benchmarks show 2x throughput gain from parallel agents on RTX 5090 with Qwen 3.6 35B
A Reddit user benchmarked LM Studio with Qwen 3.6 35B on an RTX 5090, finding that running at least 4 parallel agents doubles tokens/second compared to a single agent. The test with 8 configured parallel tasks showed significant throughput improvement, suggesting developers using agent frameworks like Open Code should parallelize to maximize performance.
A Reddit user benchmarked LM Studio with Qwen 3.6 35B on an RTX 5090, finding that running at least 4 parallel agents doubles tokens/second compared to a single agent. The test with 8 configured parallel tasks showed significant throughput improvement, suggesting developers using agent frameworks like Open Code should parallelize to maximize performance.
130 engagement1 sourceredditSun, Jul 12, 2026, 01:00 PM
Social·Community
NVIDIA Dynamo's Session-Aware Architecture Targets GPU Memory Waste in Waiting AI Agents
Medium article examines GPU memory waste in long-running AI agents citing NVIDIA Dynamo's session-aware design
Medium article examines GPU memory waste in long-running AI agents citing NVIDIA Dynamo's session-aware design
0 engagement1 sourcerssSun, Oct 11, 2026, 02:31 AM
Developments·Benchmark
NVIDIA claims up to 30× agentic throughput per MW on Vera Rubin, pending review
NVIDIA's AgentX benchmark replays production-style coding-agent sessions with long context, KV-cache reuse, tool gaps, and dynamic concurrency. The Vera Rubin preview result claims up to 30× higher throughput per megawatt than GB300 at a matched interactivity target, pending SemiAnalysis review. Critics note tokens/MW still doesn't measure completed work.
NVIDIA's AgentX benchmark replays production-style coding-agent sessions with long context, KV-cache reuse, tool gaps, and dynamic concurrency. The Vera Rubin preview result claims up to 30× higher throughput per megawatt than GB300 at a matched interactivity target, pending SemiAnalysis review. Critics note tokens/MW still doesn't measure completed work.
2 engagement1 sourceredditWed, Aug 26, 2026, 08:45 AM
Releases·Tool Release
Turing Engine serves 70B models on a single 24GB GPU at 3,064 tok/s
A new inference engine, Turing Engine, claims to serve 70B-parameter models like LLaMA-3.1-70B and Qwen-2.5-72B on a single 24GB GPU, achieving 3,064 tokens per second with 75% KV cache compression. It supports Unsloth checkpoints, potentially enabling high-throughput inference on consumer hardware.
A new inference engine, Turing Engine, claims to serve 70B-parameter models like LLaMA-3.1-70B and Qwen-2.5-72B on a single 24GB GPU, achieving 3,064 tokens per second with 75% KV cache compression. It supports Unsloth checkpoints, potentially enabling high-throughput inference on consumer hardware.
1 engagement1 sourceredditTue, Aug 25, 2026, 10:34 AM