--- title: "KVarN vs exllama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huawei-csl-kvarn-vs-turboderp-exllama" tools: ["huawei-csl-kvarn", "turboderp-exllama"] --- # KVarN vs exllama *GraphCanon updated Jul 12, 2026* ## Verdict Pick KVarN when license: KVarN is Apache-2.0, exllama is MIT; pick exllama when license: exllama is MIT, KVarN is Apache-2.0. [KVarN](https://arxiv.org/abs/2606.03458) reports 435 GitHub stars, 28 forks, and 7 open issues, last pushed Jun 22, 2026. [exllama](https://github.com/turboderp/exllama) has 2.9k stars, 223 forks, and 65 open issues, last pushed Sep 30, 2023. Figures are from public GitHub metadata via [KVarN's repository](https://github.com/huawei-csl/KVarN) and [exllama's repository](https://github.com/turboderp/exllama). | | [KVarN](/tools/huawei-csl-kvarn.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Tagline | KVarN is a native vLLM KV-cache quantization backend for your agents: 3-5x more context, throughput above FP16, and FP16-level accuracy. Calibration-free, one flag. | More memory-efficient rewrite of HF transformers for Llama with quantized weights | | Stars | 435 | 2,930 | | Forks | 28 | 223 | | Open issues | 7 | 65 | | Language | Python | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | AI Agents, Inference & Serving, LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [KVarN](/tools/huawei-csl-kvarn.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 19d | 1014d | | Open issues (now) | 7 | 65 | | Owner type | Organization | User | | Security scan | No lockfile | 29 low (29 low) | | Full report | [trust report](/tools/huawei-csl-kvarn/trust.md) | [trust report](/tools/turboderp-exllama/trust.md) | ## Choose when ### Choose KVarN if… - License: KVarN is Apache-2.0, exllama is MIT. - Tags unique to KVarN: agentic-ai, kv-cache, llm, llm-inference. - Also covers AI Agents. ### Choose exllama if… - License: exllama is MIT, KVarN is Apache-2.0. - Tags unique to exllama: docker container support, gpu optimization, memory efficiency, nvidia support. - exllama ships Docker support for self-hosted deployment. ## When NOT to use KVarN - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use exllama - Last GitHub push was 1015 days ago (dormant maintenance, Sep 30, 2023). Validate activity before betting a new project on exllama. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between KVarN and exllama? KVarN: KVarN is a native vLLM KV-cache quantization backend for your agents: 3-5x more context, throughput above FP16, and FP16-level accuracy. Calibration-free, one flag.. exllama: More memory-efficient rewrite of HF transformers for Llama with quantized weights. See the comparison table for live GitHub stats and shared categories. ### When should I choose KVarN over exllama? Choose KVarN over exllama when License: KVarN is Apache-2.0, exllama is MIT; Tags unique to KVarN: agentic-ai, kv-cache, llm, llm-inference; Also covers AI Agents. ### When should I choose exllama over KVarN? Choose exllama over KVarN when License: exllama is MIT, KVarN is Apache-2.0; Tags unique to exllama: docker container support, gpu optimization, memory efficiency, nvidia support; exllama ships Docker support for self-hosted deployment. ### When should I avoid KVarN? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid exllama? Last GitHub push was 1015 days ago (dormant maintenance, Sep 30, 2023). Validate activity before betting a new project on exllama. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is KVarN or exllama more popular on GitHub? exllama has more GitHub stars (2,930 vs 435). Stars measure visibility, not whether either tool fits your constraints. ### Are KVarN and exllama open source? Yes - both are open-source projects on GitHub (KVarN: Apache-2.0, exllama: MIT). ### Where can I find alternatives to KVarN or exllama? GraphCanon lists graph-backed alternatives at [KVarN alternatives](/tools/huawei-csl-kvarn/alternatives) and [exllama alternatives](/tools/turboderp-exllama/alternatives) ([KVarN markdown twin](/tools/huawei-csl-kvarn/alternatives.md), [exllama markdown twin](/tools/turboderp-exllama/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/huawei-csl-kvarn-vs-turboderp-exllama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, KVarN or exllama? KVarN: Active. exllama: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for KVarN and exllama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [KVarN trust report](/tools/huawei-csl-kvarn/trust); [exllama trust report](/tools/turboderp-exllama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huawei-csl-kvarn`](/api/graphcanon/graph?tool=huawei-csl-kvarn) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_