--- title: Llama-2-13b-chat-hf—affordable performance for AI engineers canonical: https://telnyx.com/llm-library/llama-2-13b-chat-hf --- # Llama-2-13b-chat-hf—affordable performance for AI engineers [Start building](https://telnyx.com/sign-up) ### Ask AI * [![GPT](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/chatgpt_icon-ea55e09d.jpeg?width=92&format=webp)](https://chat.openai.com/?q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Claude](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/claude_icon-c7c8eef3.webp?width=92&format=webp)](https://claude.ai/new?q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Perplexity](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/perplexity_icon-cd5b74f1.webp?width=92&format=webp)](https://www.perplexity.ai/search?q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Gemini](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/gemini_icon-4ff1cbf4.webp?width=92&format=webp)](https://www.google.com/search?udm=50&aep=11&q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Grok](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/grok_icon-e588e5bd.webp?width=92&format=webp)](https://x.com/i/grok?text=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) Llama-2-13b-chat-hf—affordable performance for AI engineers # Llama 2 Chat 13B Meta's 13B-parameter Llama 2 chat model, offering stronger benchmark performance than comparably sized open-source models with RLHF-tuned dialogue. [Start building](https://telnyx.com/sign-up)[GET Available Models](https://developers.telnyx.com/docs/inference/models) ## about Widely considered the best quality-per-FLOP trade-off in [the Llama 2 family](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf), the 13B chat model scores 54.8% on MMLU and 61.9% on TruthfulQA, closing the gap with the 70B variant far more than its parameter count would suggest. At 13 billion parameters with 40 layers and a 5120-dimension hidden state, it runs on a single consumer GPU with quantization. LicenseLLAMA 2 Community License Context window(in thousands) ## Explore Our LLM Library Discover the power and diversity of large language models available with Telnyx. Explore the options below to find the perfect model for your project. ### DeepSeek-V4-Flash-0731 OrganizationTelnyx Model NameDeepSeek-V4-Flash-0731 Taskstext generation Languages SupportedEnglish Context Length1,048,576 Parameters284.0B Model Tierunlisted Licensemit ### DeepSeek-V4.1-Flash ### gemma-2b-it ### gemma-4-26B-A4B-it ### gemma-4-31B-it ### Llama-3.3-70B-Instruct ### Meta-Llama-3.1-70B-Instruct ### Meta-Llama-3.1-8B-Instruct ### MiniMax-M2.7 ### MiniMax-M3-MXFP8 ### Kimi-K2.5 ### Kimi-K2.6 ### Kimi-K3 ### NVIDIA-NemotronLabs-VoiceChat-11B ### gpt-oss-120b ### Laguna-S-2.1 ### Qwen3-235B-A22B ### Qwen3.8-27B ### GLM-5.1-FP8 ### GLM-5.2 ### GLM-5.3 ### GLM-5.3-Flash ### claude-haiku-4-5 ### gemini-2.5-flash ### gemini-3.7-flash ### gpt-oss-120b ### gpt-4.1 ### gpt-4o ### gpt-4o-mini ### gpt-5 ### gpt-5.1 ### gpt-5.2 ### gpt-5.4 ### gpt-5.4-mini ### gpt-5.6-luna ### gpt-5.6-sol ### gpt-6-luna ### gpt-live-1 HOW IT WORKS ## Selecting LLMs for Voice AI [GET Available Models](https://developers.telnyx.com/docs/inference/models) ### Create an account Sign-up to the Telnyx platform to get access to our suite of intuitive AI tools. ### Choose Llama 2 Chat 13B In the [AI Playground](https://portal.telnyx.com/#/app/next/ai/ai-playground "Telnyx AI Playground") select Llama 2 Chat 13B from the dropdown. ### Enter your API key If you’re using a proprietary model, you’ll need your API key before prompting the LLM. Our vast selection of OS models allow you to get started right away. ### Prompt the LLM Get responses from an LLM in minutes, and scale with our [OpenAI-compatible APIs](https://developers.telnyx.com/docs/inference/getting-started "Inference API Quickstart"). Step 1/4 **RESOURCES** ## Get started Check out our helpful tools to help get you started. * ### Test in the portal Easily browse and select your preferred model in the AI Playground. [Test today](https://portal.telnyx.com/#/ai/assistants) ## Sign up and start building [Sign up](https://telnyx.com/sign-up)[Contact sales](https://telnyx.com/contact-us) ## faqs ### What is Llama 2 7B Chat? Llama 2 13B Chat is the mid-sized model in Meta's Llama 2 family, offering more capability than the 7B variant while remaining manageable for [single-GPU deployment](https://telnyx.com/llm-library/llama-2-13b-chat-hf). It is fine-tuned for dialogue using RLHF. ### Is Llama 2 13B free? Yes, Llama 2 13B is released under Meta's community license for free commercial use. Weights are available on [Hugging Face](https://huggingface.co/meta-llama/Llama-2-13b-chat) and through hosted inference platforms. 4096 ## Use cases for Llama 2 Chat 13B 1. **Single-GPU quality inference:** At 13B parameters with quantization, it runs on a single consumer GPU while scoring nearly 10 MMLU points above the 7B variant, offering the best quality-per-FLOP in the Llama 2 family. 2. **Trustworthy factual dialogue:** Scoring 61.9% on TruthfulQA with RLHF tuning, it provides more reliable factual responses than many larger models for knowledge-intensive chat applications. 3. **Domain fine-tuning baseline:** Its position as the sweet spot between the 7B and 70B makes it the most practical Llama 2 variant for organizations fine-tuning on domain-specific datasets with limited compute. ## Quality Arena Elo1063 MMLU53.6 MT Bench6.65 Llama 2 13B Chat scores 54.8% on MMLU (5-shot), roughly 10 points above Llama 2 7B Chat (45.3%) and 14 points below Llama 2 70B Chat (68.9%) on the same sheet. On TruthfulQA it reaches 61.9%, competitive with much larger models due to RLHF tuning. It represents the best quality-per-FLOP tradeoff in the Llama 2 family, running on a single consumer GPU with quantization. **Mistral 7B Instruct v0.2** 1072 **GPT-3.5 Turbo-1106** 1068 **Llama 2 Chat 13B** 1063 **Dolphin 2.5 Mixtral 8X7B** 1063 **Zephyr 7B beta** 1053 ## pricing The cost for running the model with Telnyx Inference is $0.0003 per 1,000 tokens. For instance, analyzing 1,000,000 customer chats, assuming each chat is 1,000 tokens long, would cost $300. ## What's Twitter saying? * **Strong benchmark performance**: [Developers praise Llama 2 13B Chat](https://telnyx.com/llm-library/llama-2-13b-chat-hf) for its excellent quality-per-FLOP trade-off, scoring 54.8% on MMLU and 61.9% on TruthfulQA, outperforming comparably sized open-source models. * **Local inference feasibility**: [Tech users report successful runs on](https://github.com/abetlen/llama-cpp-python/discussions/845) consumer hardware like M1/M2 Macs at 20-25 tokens/second, though sensitive to prompt structure and slower than GPT-4. * **Output inconsistency noted**: [Community tests highlight inconsistent results when](https://andlukyane.com/blog/paper-review-llama2) repeating the same prompt, such as varying pros/cons summaries from restaurant reviews. | Organization | Model Name | Tasks | Languages Supported | Context Length | Parameters | Model Tier | License | | ------------ | ----------------------------------------------------------------------- | ---------------- | ------------------- | -------------- | ---------- | ---------- | ----------------- | | Telnyx | DeepSeek-V4-Flash-0731 | text generation | English | 1,048,576 | 284.0B | unlisted | mit | | Telnyx | DeepSeek-V4.1-Flash | text generation | English | 1,048,576 | 552.0B | small | mit | | google | [gemma-2b-it](https://telnyx.com/llm-library/gemma-2b-it) | text generation | English | 8,192 | 2.5B | small | gemma | | google | gemma-4-26B-A4B-it | text to-text | Multilingual | 262,144 | 25.8B | small | apache-2.0 | | google | gemma-4-31B-it | text generation | Multilingual | 131,072 | 30.7B | small | apache-2.0 | | meta-llama | [Llama-3.3-70B-Instruct](https://telnyx.com/llm-library/llama-3.3-70b-instruct) | text generation | Multilingual | 99,000 | 70.6B | large | llama3.3 | | meta-llama | [Meta-Llama-3.1-70B-Instruct](https://telnyx.com/llm-library/meta-llama-3-1-70b-instruct) | text generation | Multilingual | 99,000 | 70.6B | large | llama3.1 | | meta-llama | [Meta-Llama-3.1-8B-Instruct](https://telnyx.com/llm-library/meta-llama-3.1-8b-instruct) | text generation | Multilingual | 131,072 | 8.0B | small | llama3.1 | | minimaxai | MiniMax-M2.7 | text generation | Multilingual | 200,000 | 0 | large | minimaxai | | minimaxai | MiniMax-M3-MXFP8 | text generation | Multilingual | 1,000,000 | 428.0B | small | minimax-community | | moonshotai | [Kimi-K2.5](https://telnyx.com/llm-library/kimi-k2.5) | text generation | Multilingual | 256,000 | 1.0T | large | modified-mit | | moonshotai | Kimi-K2.6 | text generation | Multilingual | 262,144 | 1.0T | medium | modified-mit | | moonshotai | Kimi-K3 | text generation | English | 1,000,000 | 2.8M | large | MIT Modified | | nvidia | NVIDIA-NemotronLabs-VoiceChat-11B | speech to-speech | English | 8,192 | 11.1B | medium | openmdw-1.1 | | openai | [gpt-oss-120b](https://telnyx.com/llm-library/gpt-oss-120b) | text generation | Multilingual | 131,072 | 117.0B | small | apache-2.0 | | poolside | Laguna-S-2.1 | text generation | English | 1,048,576 | 117.6B | unlisted | OpenMDW-1.1 | | Qwen | [Qwen3-235B-A22B](https://telnyx.com/llm-library/qwen3-235b-a22b) | text generation | English | 32,768 | 235.1B | large | apache-2.0 | | Telnyx | Qwen3.8-27B | text generation | English | 262,144 | 27.0B | small | Apache 2.0 | | zai-org | GLM-5.1-FP8 | text generation | Multilingual | 202,752 | 753.9B | large | mit | | zai-org | GLM-5.2 | text generation | English | 1,000,000 | 753.9B | medium | MIT | | Telnyx | GLM-5.3 | text generation | English | 1,048,576 | 753.0B | medium | glm-5.3 | | zai-org | GLM-5.3-Flash | text generation | English | 1,048,576 | 320.0B | small | mit | | anthropic | [claude-haiku-4-5](https://telnyx.com/llm-library/claude-haiku-4-5) | text generation | Multilingual | 400,000 | 0 | large | openai | | google | [gemini-2.5-flash](https://telnyx.com/llm-library/gemini-2.5-flash) | text generation | Multilingual | 400,000 | 0 | large | openai | | google | gemini-3.7-flash | text generation | Multilingual | 400,000 | 0 | large | openai | | groq | [gpt-oss-120b](https://telnyx.com/llm-library/gpt-oss-120b) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-4.1 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | [gpt-4o](https://telnyx.com/llm-library/gpt-4-omni) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | [gpt-4o-mini](https://telnyx.com/llm-library/gpt-4o-mini) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.1 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | [gpt-5.2](https://telnyx.com/llm-library/gpt-5.2) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.4 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.4-mini | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.6-luna | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.6-sol | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-6-luna | text generation | Multilingual | 1,050,000 | 0 | large | openai | | openai | gpt-live-1 | speech to-speech | Multilingual | 400,000 | 0 | large | openai | * ### Explore the docs Don’t wait to scale, start today with our public API endpoints. [Get started](https://developers.telnyx.com/api-reference/assistants/list-assistants) * ### Stay up to date Keep an eye on our AI changelog so you don't miss a beat. [See updates](https://telnyx.com/release-notes) ### How to use Llama 2 7B Chat HF? Llama 2 13B Chat can be loaded through Hugging Face Transformers or deployed locally with tools like Ollama. For production workloads, [hosted inference](https://telnyx.com/llm-library/llama-2-13b-chat-hf) provides API access without GPU management. ### Does Llama have a chat interface? Meta provides Llama models through the API and open weights, not a consumer chat interface. Third-party tools and [inference providers](https://support.telnyx.com/en/articles/8344129-get-started-with-telnyx-storage-inference-guide) offer chat-style interfaces for interacting with Llama models. ### Is Llama 2 13B better than 7B? Llama 2 13B provides noticeably better reasoning and factual accuracy than the 7B variant, at roughly double the compute cost. For tasks requiring more nuanced responses, the [13B model](https://telnyx.com/llm-library/llama-2-13b-chat-hf) is worth the additional resources. ### How much VRAM does Llama 2 13B need? Llama 2 13B requires approximately 26GB of VRAM at full precision, or 8-10GB with 4-bit quantization. An RTX 4090 or A6000 can handle the quantized version for [local development](https://huggingface.co/meta-llama/Llama-2-13b-chat).