Comparison
KVarN vs exllama
Verdict
Pick KVarN if kVarN amplifies AI agent capabilities via vLLM KV-cache quantization for extended context and throughput without sacrificing accuracy; pick exllama if exLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards.
Markdown twin · KVarN alternatives · exllama alternatives
GraphCanon updated Sep 20, 2026
8views this month
Trust & integrity
| Signal | KVarN | exllama |
|---|---|---|
| Maintenance | Steady (89d since push) As of Sep 20, 2026 · github_public_v1 | Dormant (1072d since push) As of Sep 7, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 7, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- KVarN
- vLLM KV-cache quantization backend for AI agents
- exllama
- Memory-efficient rewrite of HF transformers for Llama with quantized weights
Stars
- KVarN
- 497
- exllama
- 2.9k
Forks
- KVarN
- 36
- exllama
- 220
Open issues
- KVarN
- 11
- exllama
- 65
Language
- KVarN
- Python
- exllama
- Python
Adopt for
- KVarN
- KVarN amplifies AI agent capabilities via vLLM KV-cache quantization for extended context and throughput without sacrificing accuracy.
- exllama
- ExLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards.
Persona
- KVarN
- -
- exllama
- -
Runtime
- KVarN
- -
- exllama
- -
License
- KVarN
- Apache-2.0
- exllama
- MIT
Last pushed
- KVarN
- Jun 22, 2026
- exllama
- Sep 30, 2023
Categories
- KVarN
- Inference & Serving
- exllama
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- KVarN
- Steady (60%)
- exllama
- Dormant (18%)
Days since push
- KVarN
- 89d
- exllama
- 1072d
Open issues (now)
- KVarN
- 11
- exllama
- 65
Stars delta
- KVarN
- +55 (30d)
- exllama
- +6 (30d)
Open issues delta
- KVarN
- +3 (30d)
- exllama
- 0 (30d)
Owner type
- KVarN
- Organization
- exllama
- User
OSV dependency advisories
- KVarN
- No lockfile (source not queried)
- exllama
- Published findings
Full report
- KVarN
- Trust report
- exllama
- Trust report
Choose KVarN if…
- License: KVarN is Apache-2.0, exllama is MIT.
- Tags unique to KVarN: agentic-ai, kv-cache, llm-inference, long-context.
- For applications needing over threefold to fivefold increase in context length compared to FP16.
When NOT to use KVarN
- If project constraints do not allow for Apache-2.0 licensing terms.
- Projects that cannot benefit from a quantization backend, such as those requiring non-variable length model support.
Choose exllama if…
- License: exllama is MIT, KVarN is Apache-2.0.
- Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu.
- Also covers LLM Frameworks.
- exllama ships Docker support for self-hosted deployment.
- - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support.
When NOT to use exllama
- - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better.
- - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts).
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (huawei-csl/KVarN) · observed Sep 20, 2026
- GitHub forks (huawei-csl/KVarN) · observed Sep 20, 2026
- Last push (huawei-csl/KVarN) · observed Jun 22, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (turboderp/exllama) · observed Sep 20, 2026
- GitHub forks (turboderp/exllama) · observed Sep 20, 2026
- Last push (turboderp/exllama) · observed Sep 30, 2023
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: KVarN 497 · exllama 2.9k (synced Sep 20, 2026).
Common questions
- What is the difference between KVarN and exllama?
- KVarN: vLLM KV-cache quantization backend for AI agents. exllama: 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-inference, long-context; For applications needing over threefold to fivefold increase in context length compared to FP16.
- 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, llama model, memory-efficient, nvidia gpu; Also covers LLM Frameworks; exllama ships Docker support for self-hosted deployment; - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support.
- When should I avoid KVarN?
- If project constraints do not allow for Apache-2.0 licensing terms. Projects that cannot benefit from a quantization backend, such as those requiring non-variable length model support.
- When should I avoid exllama?
- - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better. - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts).
- Is KVarN or exllama more popular on GitHub?
- exllama has more GitHub stars (2,943 vs 497). 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 and exllama alternatives (KVarN markdown twin, exllama markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, KVarN or exllama?
- KVarN: Steady. 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; exllama trust report.