Local Setup: Small Language Model vs. Quantized Large Model

 

TLDR

For those of you not familar with the term (TLDR: Too Long;Didn't Read)

Running efficient LLMs locally for coding tasks on an older local hardware (e.g. RTX 3080 (10GB VRAM)) requires careful optimization to balance model capability with hardware constraints. After extensive research into the latest developments through 2025, In most cases, native small models  outperform heavily quantized large models but not always. In short, further research is needed. As most things you find with the latest transformer based research, nothing seems conclusive in June 2025.

Local Agent - Native vs. Quantized

Deploying these models locally on personal computers (PCs) for specialized tasks, such as coding assistance tailored to a specific subject or project, presents a considerable challenge. The primary constraints are hardware limitations, particularly VRAM, RAM, and processing power, which often preclude the use of the largest, most capable models. 

This necessitates an exploration of strategies to enable efficient local LLM operation without substantial degradation in performance for coding-specific applications. The core problem lies in balancing model capability (accuracy, reasoning, specialization) with resource consumption (memory footprint, inference speed) on typical PC hardware.

Case for Native Small Language Model

Small Language Model + RAG + Distillation.

Native small models dominate the 10GB VRAM landscape

The coding model landscape has undergone a revolution in 2024-2025, with small models achieving performance that rivals much larger alternatives. Yi-Coder-9B-Chat leads the pack, achieving 23% on LiveCodeBench - the highest score for any model under 10B parameters, outperforming even DeepSeek-Coder-33B. This model fits comfortably within the 10GB VRAM constraint at ~9GB usage while supporting an impressive 128K token context window across 52 programming languages.

CodeGemma 7B represents another breakthrough, significantly outperforming CodeLlama 7B across diverse coding tasks while maintaining the same memory footprint (~7GB VRAM). Its superior reasoning capabilities and 2x faster inference speed make it ideal for complex code generation tasks. StarCoder2-7B, trained on 4x more data than its predecessor (67.5TB vs 6.4TB), offers exceptional multilingual support across 600+ programming languages.

For extreme efficiency, Stable Code 3B delivers CodeLlama 7B-level performance while using only 3GB VRAM - a 60% reduction in resource requirements. This makes it perfect for running multiple models simultaneously or leaving headroom for other applications.

Quantization reveals diminishing returns for large models

While quantization technologies have advanced significantly, heavily quantized large models struggle to maintain code quality. 4-bit quantization represents the practical limit for coding tasks, maintaining 88-95% of baseline performance depending on the model architecture. More aggressive quantization (3-bit or 2-bit) causes severe degradation, often producing syntactically incorrect or logically flawed code.

The most effective quantization methods for RTX 3080 deployment include:

• AWQ (Activation-aware Weight Quantization): Preserves ~1% of critical weights at higher precision, achieving best-in-class 4-bit performance for coding tasks
• EXL2: Enables mixed-precision quantization with variable bit-width per layer, maintaining 96-99% of full precision coding performance
• GGUF Q5_K_M: Optimal for CPU-GPU hybrid scenarios when leveraging system RAM, maintaining 95-98% performance

When attempting to fit larger models like CodeLlama 34B into 10GB VRAM, the required extreme quantization (Q2 or Q3) results in performance worse than native 7B models while consuming similar resources. This makes heavily quantized large models a poor choice for the RTX 3080 platform.

Hybrid CPU-GPU inference maximizes the 64GB RAM advantage

The combination of 10GB VRAM and 64GB system RAM opens opportunities for sophisticated layer offloading strategies. Optimal configurations offload 20-25 layers to GPU for 13B models, keeping attention and embedding layers on GPU while relegating feed-forward networks to CPU. This approach enables running 13B models with 4-bit quantization at 80-120 tokens/second - fast enough for interactive coding assistance.

Memory bandwidth becomes critical for hybrid inference. Using DDR4-3200 or higher RAM with proper configuration (`--mlock` to prevent swapping, `--threads` matching physical cores) ensures smooth operation. The latest llama.cpp implementations with memory mapping reduce active VRAM usage by 15-20%, providing additional headroom for larger context windows.

RAG transforms local coding assistants

Retrieval Augmented Generation dramatically enhances local coding LLMs by providing project-specific context. ChromaDB offers the simplest setup for development environments, requiring only 4-8GB RAM for moderate codebases. For production deployments, Qdrant provides advanced filtering and real-time updates with REST API access.

Effective RAG implementation for coding requires specialized strategies:

• Code-aware chunking: Using Tree-sitter for syntax-aware splitting maintains code structure integrity
• Semantic embeddings: CodeBERT or UniXcoder provide superior code understanding compared to general-purpose embeddings
• Hierarchical indexing: Organizing code by project → module → function enables efficient retrieval
• Context enhancement: Adding natural language descriptions to code snippets improves retrieval accuracy

The combination of a 7B coding model with well-implemented RAG often outperforms standalone 34B models for project-specific tasks, while using a fraction of the resources.

Model distillation enables custom optimization without fine-tuning

Recent advances in distillation techniques allow extracting specialized capabilities from large models into smaller, deployment-friendly versions. Step-by-step distillation captures not just outputs but reasoning traces, enabling 770M parameter models to outperform 540B alternatives on specific tasks. This approach is particularly effective for coding, where intermediate reasoning steps are crucial.

Practical distillation for coding models involves:

• Using teacher models like DeepSeek-Coder-33B to generate high-quality outputs with probability distributions
• Capturing step-by-step reasoning including problem understanding, concept identification, and implementation details
• Training smaller student models (7B range) on this enriched dataset
• Applying task-specific pruning to optimize for code completion vs. generation

Deployment frameworks: Ollama

Among deployment options, Ollama emerges as the clear winner for RTX 3080 setups. Its single-command deployment (`ollama run codellama`), REST API compatibility, and efficient memory management make it ideal for both development and production. Recent updates add tool calling support for function execution and improved multimodal capabilities.

For those preferring graphical interfaces, LM Studio provides drag-and-drop model management with smart GPU offloading. Its visual parameter tuning helps optimize performance without command-line expertise. Text-generation-webui (Oobabooga) offers the most features but requires more technical knowledge and suffers from stability issues.

The optimal deployment stack for RTX 3080 combines:

• Primary framework: Ollama for model serving
• IDE integration: Numerous options as an extension for VS Code
• API layer: vLLM for high-throughput scenarios among other options.
• Monitoring: Langfuse and other options for comprehensive observability

Practical architecture for production-ready coding agents

A multi-model architecture maximizes the RTX 3080's capabilities by allocating different models to specific tasks:

Hypothetical architecture using task specific language models:

For example:

• DeepSeek Coder 1.3B for rapid code completion (1.2GB VRAM, 50-80 tokens/second)
• CodeGemma 7B** for code analysis and refactoring (7GB VRAM, balanced performance)
• CodeLlama 13B Q4** for complex generation tasks (8.5GB VRAM, maximum capability)

Notable developments in 2024-2025:

• 1-bit quantization breakthroughs: BitNet's ternary weights (-1, 0, 1) enable running sophisticated models on minimal hardware
• Speculative decoding: Small models draft tokens verified by larger models, achieving 2-3x speedups
• DeepSeek R1: State-of-the-art reasoning capabilities in locally deployable formats
• Mixed-precision scaling: Larger models tolerate more aggressive quantization, optimizing the accuracy-efficiency tradeoff

Primary Models for Consideration

• Yi-Coder-9B-Chat or CodeGemma 7B for general coding tasks
• DeepSeek Coder 1.3B for rapid completion
• CodeLlama 13B Q4_K_M for complex generation (with CPU offloading)

# Ollama setup
ollama pull yi-coder:9b-chat
ollama pull codegenmma:7b
ollama pull deepseek-coder:1.3b

# Optimal runtime configuration
export CUDA_VISIBLE_DEVICES=0
export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512

# Model-specific settings
ollama run yi-coder:9b-chat \
  --gpu-layers 32 \
  --ctx-size 8192 \
  --batch-size 512 \
  --threads 8

Case for Quantization

Model Quantization

Quantization is a critical model compression technique that reduces the numerical precision of a model's weights and, in some cases, activations, leading to smaller model sizes and potentially faster inference with minimal performance loss.6

Principles of Quantization (FP32 to INT8/INT4)

The core idea is to represent model parameters (weights and/or activations) using lower-precision data types, such as 8-bit integers (INT8) or 4-bit integers (INT4), instead of the original high-precision 32-bit floating-point (FP32) or 16-bit floating-point (FP16) formats.6 This conversion typically involves a mapping scheme, such as affine quantization, where an FP32 value x is mapped to a quantized integer xq​ using a scaling factor S and a zero-point Z: xq​=round(x/S+Z).7 While weights and activations are quantized, they are often dequantized during inference for computations, requiring storage of scaling factors.7

Benefits: Reduced Size, Faster Inference, Lower Energy

Quantization offers several advantages:

• Reduced Model Size: Storing weights in lower precision significantly decreases the overall model size, reducing storage requirements and memory footprint.7 For example, quantizing a 7B parameter model to 4-bit can reduce VRAM needs from ~14-28GB (FP16/FP32) to 4-6GB.1
• Faster Inference: Lower computational complexity from smaller data representations can speed up inference times, especially on hardware with optimized support for lower-precision arithmetic.8
• Lower Energy Consumption: Reduced computational load often translates to lower energy use, making models more cost-effective and environmentally friendly.8
• Improved Deployment Flexibility: Enables deployment on a wider range of hardware, including edge devices and mobile platforms.8

Types: PTQ vs. QAT and Impact on Accuracy

There are two main approaches to quantization:

• Post-Training Quantization (PTQ): Quantization is applied to an already trained model. PTQ is generally easier and faster to implement, requiring less training data. However, it can lead to a slight loss of accuracy as the model isn't trained to be robust to quantization effects.Most PTQ methods are evaluated on pre-trained LLMs, with less clarity on instruction-tuned LLMs.
• Quantization-Aware Training (QAT): Quantization is incorporated into the model training or fine-tuning process. The model learns to minimize performance loss caused by quantization by simulating quantization effects during both forward and backward passes. QAT generally results in better accuracy preservation than PTQ but is more resource-intensive. The extent of accuracy loss depends on factors like the quantization scheme, model architecture, and training dataset. Studies show that 4-bit quantization can often maintain performance comparable to non-quantized counterparts. However, aggressive quantization (e.g., 2-bit or 3-bit) can lead to more significant performance degradation.
• Popular Quantization Techniques for Local LLMs: GGUF, GPTQ, AWQ. Several techniques have gained prominence for quantizing LLMs for local deployment:

Technique	Primary Focus	Key Characteristics	Hardware Compatibility	Performance (Mistral 7B Example)
GGUF (GPT-Generated Unified Format)	CPU & Apple Silicon (GPU offload capable)	Successor to GGML; flexible format for CPU-based inference with optional GPU layer offloading; user-friendly for model file handling.	CPU, Apple M-series, GPU (partial)	Time: 15.50s, VRAM: 0.97 GB
**GPTQ** (Generalized Post-Training Quantization	GPU inference	One-shot weight quantization (2, 3, 4, 8-bit) based on approximate second-order information; balances compression and inference speed.	Most GPU hardware (NVIDIA focused)	Time: 8.78s, VRAM: 0.11 GB
**AWQ** (Activation-Aware Weight Quantization)	GPU inference, resource-constrained devices	Protects salient weights by observing activations; excellent for instruction-tuned and multi-modal LMs; aims to reduce GPU memory and speed up token generation.	GPUs, Edge platforms	Time: 4.96s, VRAM: 0.00 GB

Natively Small vs. Quantized Large Models: Performance Trade-offs

Choosing between a natively small language model (SLM) and a quantized version of a large language model (LLM) for local deployment involves a careful evaluation of performance trade-offs across accuracy, inference speed, and memory consumption.

Defining Small

In the context of models runnable on local PCs, "small" typically refers to models with parameter counts ranging from a few million up to around 7-8 billion (e.g., Phi-3 Mini 3.8B, Mistral 7B).2 "Large" models, in this local context, would be those that, in their unquantized state, would exceed typical consumer hardware VRAM (e.g., 13B, 30B, 70B parameter models) but become feasible through quantization.1 For instance, a 70B parameter model is unequivocally large, while a 7B model might be considered small or medium depending on the specific hardware and comparison point.

Impact of Quantization on LLM Performance

Quantization aims to reduce model size and improve efficiency, but it inherently involves a trade-off with model performance.

 Accuracy vs. Model Size Reduction

Quantization reduces the precision of model weights and activations, leading to significant model size reduction.7 The general trend observed is that higher bit precision (e.g., 8-bit, Q8_0) yields better performance, closer to the original unquantized model, while lower bit precision (e.g., 4-bit, 2-bit) results in greater compression but potentially more noticeable performance degradation.10

Studies indicate that 4-bit quantization often represents a good balance, allowing models to retain performance comparable to their non-quantized (e.g., FP16 or BFloat16) counterparts on many benchmarks.6 For example, a thesis investigating eight LLMs across various quantization levels found that 4-bit (e.g., Q4_K) generally maintained good performance, while 2-bit (e.g., Q2_K) showed acceptable performance but with instances of substantial coherence and accuracy losses in some models. Extremely low-bit quantization (e.g., 2-bit or 3-bit) can lead to severe degradation, such as models failing to form coherent sentences, repeating themselves, or exhibiting poor contextual memory. The impact of quantization is not uniform and varies across different model architectures and the specific tasks they are evaluated on.

Perplexity as an Indicator

Perplexity is a common metric used to evaluate language models, measuring how well a model predicts a sequence of words; a lower perplexity score generally indicates better performance. Research confirms that perplexity serves as a reliable performance indicator for quantized LLMs on evaluation benchmarks.

As models are quantized to fewer bits, there is typically an upward trend in perplexity, corresponding to a potential decline in performance on downstream tasks. For instance, the Gemma 2B v1.1 model showed perplexity increasing from 30.02 (Q8_0) to 39.86 (Q2_K), while Llama 3 8B saw an increase from 8.36 (Q8_0) to 11.36 (Q2_K). However, an interesting observation is that despite a noticeable increase in perplexity, 4-bit quantized models can still perform comparably to their non-quantized counterparts on many benchmarks, suggesting that minor perplexity increases at this quantization level might not always translate to significant drops in task-specific accuracy, possibly due to the non-linear nature of some evaluation metrics.

Task-Specific Performance

The impact of quantization on task-specific performance varies significantly. Larger models generally exhibit greater resilience to aggressive quantization than smaller models, although they too can suffer performance drops at very low precision levels (e.g., 2-bit). Mid-sized models often present an optimal balance between capability, efficiency, and resource usage under quantization.

For example, in a study evaluating Qwen-Chat models, 4-bit quantized versions (using GPTQ and SpQR) of the 14B and 72B parameter models consistently outperformed the non-quantized (BFloat16) 7B parameter model on benchmarks like MMLU (knowledge and reasoning), C-EVAL (Chinese evaluation), and GSM8K (math word problems).26 On MMLU, Qwen-7B-Chat (BF16) scored 55.80%, while Qwen-14B-Chat (4-bit GPTQ) scored 63.42% and Qwen-72B-Chat (4-bit GPTQ) scored 73.81%. Similar trends were observed for GSM8K.

However, performance can be task-dependent. For instance, on CRUXEval (code comprehension), performance remained fairly constant across models and quantization levels in one study, while on MuSR (text comprehension), performance was generally unaffected by quantization precision, with exceptions for some models like Phi 3 at very low bit counts.

Model	GPU	Prompt Processing Speed (t/s)	Generation Speed (t/s)	Time to First Token (ms)
Meta Llama 3.1 8B Instruct Q4_K_M	NVIDIA GeForce RTX 4090	6749	91.9	197
Meta Llama 3.1 8B Instruct Q4_K_M	NVIDIA GeForce RTX 3090	3727	106	345
Meta Llama 3.1 8B Instruct Q4_K_M	NVIDIA GeForce RTX 4070 Ti SUPER	4222	77.6	316
Qwen2.5 14B Instruct Q4_K_M	NVIDIA GeForce RTX 4090	3246	42.3	416
Qwen2.5 14B Instruct Q4_K_M	NVIDIA GeForce RTX 3090	2037	61.1	631
Qwen2.5 14B Instruct Q4_K_M	NVIDIA GeForce RTX 4070 Ti SUPER	2454	50.4	535
Llama 3 70B Q4_K_M	2x NVIDIA GeForce RTX 4090 (llama.cpp)	905.38 (prompt eval)	19.06	N/A

Memory Consmuption (VRAM and RAM)

Quantized Large Models: Significant VRAM reduction

    

Quantization dramatically reduces memory requirements. A 7B parameter model, which might take 14GB in FP16, could need only 4-6 GB of VRAM when quantized to 4-bits. This makes larger models accessible on consumer GPUs that would otherwise be incapable of loading them. For instance, a Llama 3 70B Q4_K_M GGUF model has a file size of about 42.5 GB, indicating its VRAM requirement. AWQ 4-bit quantization can bring a 70B model down to around 36.7 GB VRAM.

    

Natively Small Models: Lower baseline VRAM/RAM needs

Natively smaller models naturally have lower memory footprints. For example, Microsoft's Phi-2 (2.7B parameters) requires a minimum of 3.1 GB VRAM/RAM, and Mistral 7B needs about 6 GB VRAM/RAM. This makes them suitable for a wider range of local hardware, including systems with less powerful GPUs or even CPU-only inference setups, albeit with slower performance.

Quantized Large for Local Coding Agents

• Performance vs. Size: Larger models inherently possess more knowledge and sophisticated reasoning capabilities due to their extensive parameter counts and training data. Quantization, particularly 4-bit (e.g., Q4_K_M GGUF), allows these larger models to run on hardware with limited VRAM while often retaining a substantial portion of their original performance. Studies and community consensus suggest that a larger model quantized to 4-bits generally outperforms a natively smaller model, even if the smaller model is less aggressively quantized or unquantized, provided they have similar memory footprints.6 For instance, a 4-bit quantized 14B parameter model can outperform a non-quantized 7B model on various benchmarks.
• Sensitivity to Quantization: Smaller models (e.g., <7B) tend to be more sensitive to aggressive quantization (below 4-bit), potentially suffering significant performance degradation. Larger models exhibit greater resilience, making them better candidates for achieving a balance of capability and reduced size through methods like 4-bit quantization.
• Inference Speed: Natively small models are generally faster due to fewer computations. Quantized larger models can also be fast if hardware support for low-precision arithmetic is good, but can sometimes be slower than their unquantized counterparts if not optimally implemented or supported by the hardware.
• Resource Consumption: Quantization is highly effective at reducing VRAM requirements, making larger models accessible on consumer hardware. A 70B model requiring >100GB in FP16 can be reduced to ~40GB at 4-bit quantization. Natively small models have inherently lower baseline memory needs.