Sign up to save your podcasts
Or
Share 006: Running Local LLMs on Consumer GPUs
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
006: Running Local LLMs on Consumer GPUs
Another one from notebook lm. Its 136 sources. Im setting up a RAG servee at work and need to know more about local models due to security restrictions.
below is my prompt:
---
System Role: Act as an AI Infrastructure Engineer and generate an actionable research report based strictly on Hugging Face data (model cards, discussions, metrics) for local GGUF models running via LM Studio/llama.cpp on Windows.
Target Use Case: Software engineering (C, C++, C#, Lua, Unreal Blueprints, Unity YAML) and local RAG.
Hardware Targets: Tier A (10GB VRAM / RTX 3080), Tier B (24GB VRAM / RTX 3090), Tier C (48GB VRAM / Pro GPU).
Required Deliverables:
Model Taxonomy & Lineage: Define current classes (Generative LLMs, Embeddings, Rerankers, Vision) mapping to LM Studio UI. Detail provenance for major architectures (e.g., Llama, Mistral, Qwen, Phi).
Tiered Model Shortlist: Top coding/generalist models per VRAM tier. Specify exact HF name, parameter count, Q4_K_M/Q5_K_M VRAM footprint, recommended vs. max context windows, and HF download/like metrics (with dates).
Throughput Metrics (Tokens/sec): Provide throughput estimates per GPU tier. Compare Q4 vs Q5 performance. Include test parameters (context length, batch size, prompt vs. gen mix).
Mini-Guides (Code & RAG): For each model, detail: strengths/failure modes, context degradation thresholds (10k+ lines), chunking strategies, and optimal LM Studio sampling configurations (temperature, top_p, min_p, repetition penalty).
Memory & Scaling Architecture: * Clarify LM Studio multi-model placement (CPU vs GPU offloading interactions).
Deploy A: Single GPU optimization.
Deploy B: 48GB Multi-GPU pro server (Compare vLLM vs TensorRT-LLM vs llama.cpp; explain tensor/pipeline parallelism penalties).
Deploy C: 6x8GB commodity rig OS/software strategies.
KPIs & Capabilities: Define capacity metrics (TTFT, concurrency, queue depth). Quantify the throughput penalty vs. quality gain of "reasoning" models. Explain the mechanical reality of file/image attachments (native multimodality vs text conversion).
This episode includes AI-generated content.
below is my prompt:
---
System Role: Act as an AI Infrastructure Engineer and generate an actionable research report based strictly on Hugging Face data (model cards, discussions, metrics) for local GGUF models running via LM Studio/llama.cpp on Windows.
Target Use Case: Software engineering (C, C++, C#, Lua, Unreal Blueprints, Unity YAML) and local RAG.
Hardware Targets: Tier A (10GB VRAM / RTX 3080), Tier B (24GB VRAM / RTX 3090), Tier C (48GB VRAM / Pro GPU).
Required Deliverables:
Model Taxonomy & Lineage: Define current classes (Generative LLMs, Embeddings, Rerankers, Vision) mapping to LM Studio UI. Detail provenance for major architectures (e.g., Llama, Mistral, Qwen, Phi).
Tiered Model Shortlist: Top coding/generalist models per VRAM tier. Specify exact HF name, parameter count, Q4_K_M/Q5_K_M VRAM footprint, recommended vs. max context windows, and HF download/like metrics (with dates).
Throughput Metrics (Tokens/sec): Provide throughput estimates per GPU tier. Compare Q4 vs Q5 performance. Include test parameters (context length, batch size, prompt vs. gen mix).
Mini-Guides (Code & RAG): For each model, detail: strengths/failure modes, context degradation thresholds (10k+ lines), chunking strategies, and optimal LM Studio sampling configurations (temperature, top_p, min_p, repetition penalty).
Memory & Scaling Architecture: * Clarify LM Studio multi-model placement (CPU vs GPU offloading interactions).
Deploy A: Single GPU optimization.
Deploy B: 48GB Multi-GPU pro server (Compare vLLM vs TensorRT-LLM vs llama.cpp; explain tensor/pipeline parallelism penalties).
Deploy C: 6x8GB commodity rig OS/software strategies.
KPIs & Capabilities: Define capacity metrics (TTFT, concurrency, queue depth). Quantify the throughput penalty vs. quality gain of "reasoning" models. Explain the mechanical reality of file/image attachments (native multimodality vs text conversion).
This episode includes AI-generated content.
View all episodes
By News ReDownloadAnother one from notebook lm. Its 136 sources. Im setting up a RAG servee at work and need to know more about local models due to security restrictions.
below is my prompt:
---
System Role: Act as an AI Infrastructure Engineer and generate an actionable research report based strictly on Hugging Face data (model cards, discussions, metrics) for local GGUF models running via LM Studio/llama.cpp on Windows.
Target Use Case: Software engineering (C, C++, C#, Lua, Unreal Blueprints, Unity YAML) and local RAG.
Hardware Targets: Tier A (10GB VRAM / RTX 3080), Tier B (24GB VRAM / RTX 3090), Tier C (48GB VRAM / Pro GPU).
Required Deliverables:
Model Taxonomy & Lineage: Define current classes (Generative LLMs, Embeddings, Rerankers, Vision) mapping to LM Studio UI. Detail provenance for major architectures (e.g., Llama, Mistral, Qwen, Phi).
Tiered Model Shortlist: Top coding/generalist models per VRAM tier. Specify exact HF name, parameter count, Q4_K_M/Q5_K_M VRAM footprint, recommended vs. max context windows, and HF download/like metrics (with dates).
Throughput Metrics (Tokens/sec): Provide throughput estimates per GPU tier. Compare Q4 vs Q5 performance. Include test parameters (context length, batch size, prompt vs. gen mix).
Mini-Guides (Code & RAG): For each model, detail: strengths/failure modes, context degradation thresholds (10k+ lines), chunking strategies, and optimal LM Studio sampling configurations (temperature, top_p, min_p, repetition penalty).
Memory & Scaling Architecture: * Clarify LM Studio multi-model placement (CPU vs GPU offloading interactions).
Deploy A: Single GPU optimization.
Deploy B: 48GB Multi-GPU pro server (Compare vLLM vs TensorRT-LLM vs llama.cpp; explain tensor/pipeline parallelism penalties).
Deploy C: 6x8GB commodity rig OS/software strategies.
KPIs & Capabilities: Define capacity metrics (TTFT, concurrency, queue depth). Quantify the throughput penalty vs. quality gain of "reasoning" models. Explain the mechanical reality of file/image attachments (native multimodality vs text conversion).
This episode includes AI-generated content.
below is my prompt:
---
System Role: Act as an AI Infrastructure Engineer and generate an actionable research report based strictly on Hugging Face data (model cards, discussions, metrics) for local GGUF models running via LM Studio/llama.cpp on Windows.
Target Use Case: Software engineering (C, C++, C#, Lua, Unreal Blueprints, Unity YAML) and local RAG.
Hardware Targets: Tier A (10GB VRAM / RTX 3080), Tier B (24GB VRAM / RTX 3090), Tier C (48GB VRAM / Pro GPU).
Required Deliverables:
Model Taxonomy & Lineage: Define current classes (Generative LLMs, Embeddings, Rerankers, Vision) mapping to LM Studio UI. Detail provenance for major architectures (e.g., Llama, Mistral, Qwen, Phi).
Tiered Model Shortlist: Top coding/generalist models per VRAM tier. Specify exact HF name, parameter count, Q4_K_M/Q5_K_M VRAM footprint, recommended vs. max context windows, and HF download/like metrics (with dates).
Throughput Metrics (Tokens/sec): Provide throughput estimates per GPU tier. Compare Q4 vs Q5 performance. Include test parameters (context length, batch size, prompt vs. gen mix).
Mini-Guides (Code & RAG): For each model, detail: strengths/failure modes, context degradation thresholds (10k+ lines), chunking strategies, and optimal LM Studio sampling configurations (temperature, top_p, min_p, repetition penalty).
Memory & Scaling Architecture: * Clarify LM Studio multi-model placement (CPU vs GPU offloading interactions).
Deploy A: Single GPU optimization.
Deploy B: 48GB Multi-GPU pro server (Compare vLLM vs TensorRT-LLM vs llama.cpp; explain tensor/pipeline parallelism penalties).
Deploy C: 6x8GB commodity rig OS/software strategies.
KPIs & Capabilities: Define capacity metrics (TTFT, concurrency, queue depth). Quantify the throughput penalty vs. quality gain of "reasoning" models. Explain the mechanical reality of file/image attachments (native multimodality vs text conversion).
This episode includes AI-generated content.